Processing or compressing n-dimensional signals with warped wavelet packets and bandelets

ABSTRACT

A method and apparatus for processing or compressing an n-dimensional digital signal by constructing a sparse representation which takes advantage of the signal geometrical regularity. The invention comprises a warped wavelet packet transform which performs a cascade of warped subband filtering along warping grids of sampling points adapted to the signal geometry. It also comprises a bandeletisation which decorrelates the warped wavelet packet coefficients to produce a sparse representation. An inverse warped wavelet packet transform and an inverse bandeletisation reconstruct a signal from its bandelet representation. The invention comprises a compression system which quantizes and codes the bandelet representation, a decompression system, a restoration system which enhances a signal by filtering its bandelet representation, and a feature vector extraction system for pattern recognition applications of a bandelet representation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the National Stage of International Application No. PCT/EP2002/014903, filed Dec. 17, 2003, the contents of which is hereby incorporated by reference in their entirety.

BACKGROUND OF INVENTION

The invention relates generally to a n-dimensional signal processing method, apparatus and computer program, and in particular to a method, apparatus and computer program useful for processing n-dimensional signals, such as one-dimensional signals, two-dimensional images or three-dimensional data or video image sequences.

The invention is particularly pertinent to the field of signal data processing and compression. Signal compression is a process which encodes signals for storage or transmission over a communication channel, with fewer bits than what is used by the uncoded signal. The goal is to reduce the amount of degradation introduced by such an encoding, for a given data rate. The invention is also relevant to signal restoration or feature etraction for pattern recognition.

In signal processing, efficient procedures often require to compute a stable signal representation which provides precise signal approximations with few non-zero coefficients. Signal compression applications are then implemented with quantization and entropy coding procedures. At high compression rates, it has been shown in S. Mallat and F. Faizon, “Analysis of low bit rate image trunsforn coding,” IEEE Trans. Signal Processing, vol. 46, pp. 1027-1042, 1998, that the efficiency of a compression algorithm essentially depends upon the ability to construct a precise signal approximation from few non-zero coefficients in the representation.

The stability requirement of the signal representation has motivated the use of bases and in particular orthogonal bases. The signal is then represented by its inner products with the different vectors of the basis. A sparse representation is obtained by setting to zero the coefficients of smallest amplitude. During the last twenty years, different signal representations have been constructed, with fast procedures which decompose the signal in a separable basis. Block transforms and in particular block cosine bases have found important applications in signal and image processing. The JPEG still image coding standard is an application which quantizes and Huffman encodes the block cosine coefficients of an image. More recendy, separable wavelet bases which compute local image variations at different scales, have been shown to provide a sparser image representation, which therefore improves the applications. These bases are particular instances of wavelet packet bases, in R. Coifinan, Y. Meyer, M. Wickerhauser, Method and apparatus for encoding and decoding using wavelet-pacles, U.S. Pat. No. 5,526,299. The JPEG image compression standard has been replaced by the JPEG-2000 standard which quantizes and encodes the image coefficients in a separable wavelet basis: “JPEG 2000, ISO/IEC 15444-1:2000,” 2000. Wavelet and wavelet packet bases are also used to compress one dimensional signals, including medical signals such as electrocardiogram (ECG) recordings, as in M. Hilton, J. Xu, Z. Xiong, “Wavelet and wavelet packet compression of electrocardiograms”, IEEE Trans. Biomed. Eng., vol. 44, pp. 394-402, May 1997. Decomposition in three dimensional wavelet bases are also used in video image compression, in S. Li and Y-Q. Zhang, in “Three-Dimensional Embedded Subband Coding with Optimized Truncation (3-D ESCOT)”, Applied and Computational Harmonic Analysis 10, 290-315 (2001), where a video sequence is decomposed with three dimensional wavelet transform performed along motion threads in time.

Signal restoration of sparse signal representations has been developed by thresholding the wavelet co-efficients of noisy signals in D. Donoho and I. Johnstone, “Ideal spatial adaptation via wavelet shrinkage,” Biometrika, vol. 81, pp. 425-455, December 1994. Applications of wavelet packet bases to deconvolution of signals are also presented in J. Kalili, S. Mallat, “Minimax restoration and deconvolution”, in Bayesia inference in wavelet based models ed. P. Muller and B. Vidakovic, Springer-Verlag, 1999. Constructing sparse representations is also important to extract features for pattern recognition This has important applications to content based signal indexing and retrieval from digital multimedia libraries and databases. Feature vectors using histograms of wavelet coefficients are used in M. K. Mandal and T. Aboulasr, “Fast wavelet histogiam techniques for image indexing”, Computer Vision and Image Understanding, vol. 75, no. 1/2,pp. 99-110, August 1999.

The main limitation of bases such as block cosine bases, wavelet bases or more generally wavelet packet bases, currently used for signal representation, is that these bases are composed of vectors having a fixed geometry which is not adapted to the geometry of signal structures. For onenensional signals such as ECG, which are quasi-periodic, adapting the basis to the varying period allows one to take advantage of the redundancy due to the existence of a periodicity in the signal. In images, edges often correspond to piece-wise regular curves which are therefore geometrically regular. In higher dimensional signals such as video sequences, edges and singularities often belong to manifolds that are also geometrically reular. Constructing bases that take advantage of this geometrical regularity can considerably improve the efficiency of signal representations and hence improve applications such as compression, restoration and feature reaction.

In E. Le Pennec and S. Mallat, “Method and apparatus for processing or compressing dimensional signals by foveal filtering along trajectories”U.S. patent application Ser. No. 09/834,587, filed Apr. 13, 2001, and in E. Le Pennec, S. Mallat, “Image Compression with Geometrical Wavelets”, Proceeding of International Conf. on Image Processing, Vancouver, September 2000, part of the signal information is represented with wavelet foveal filters that follow foveal trajectories adapted to the geometry of the signal. The wavelet foveal coefficients are then decorrelated with linear operators that compute bandelet coefficients. The edgeprint representation of Dragottia and Vetterli, in “Footprints and edgeprints for image denoising and compression”, Proceedings of the International Conference on Image Processsing, Thessaloniki, October 2001, use a similar strategy with footprint wavelet vectors that follow edges computed from the image. Foveal bandelets and edgeprints do not provide a complete signal representation, and it is therefore necessary to incorporate a residual signal to reconstruct the original signal, which is a source of inefficiency for data compression and restoration applications.

Accordingly, there exists a need in the art for improving signal processing, by computing sparse representations by taking advantage of the signal geometrical regularity, from which one can reconstruct precise signal approximations with fast and numerically stable procedures and apply it to signal compression, restoration and pattern recognition.

SUMMARY OF THE INVENTION

It is a primary object of this invention to devise a method and means to construct a sparse and stable warped wavelet packet representation of n-dimensional signals by taking advantage of the regularity of their geometrical structure. A further object is to a compute bandelet representation from the warped wavelet packet representation, with an efficient bandeletisation adapted to the signal geometry. It is yet another object of this invention to build a system that compresses signals by quantizing and encoding the coefficients of this sparse bandlet representation. Another object of this invention is to restore signals by processing the coefficients of this bandelet representation. Another object of this invention is to use the bandelet representation for signal feature extraction for pattern recognition systems.

The invention includes a warped wavelet packet processor that computes an n-dimensional warped wavelet packet transform including warped wavelet packet coefficients and wavelet packet warping grids, from an n-dimensional digital input signal, wherein n is a positive integer. It comprises the steps of providing an n-dimensional warped signal including n-dimensional warped coefficients and n-dimensional signal warping grids; and computing said warped wavelet packet transform of said warped signal, with a binary tree where each node performs a one-dimensional warped subband processing along a particular dimension d, with 1≦d≦n. In dimension n≧2, said warped subband processing have a phase alignment coherent with said signal warping grids.

The invention also includes an inverse warped wavelet packet processor that computes an n-dimensional digital output signal from an n-dimensional warped wavelet packet transform It comprises the steps of: computing a warped signal including n-dimensional warped coefficients and n-dimensional signal warping grids, from said n-dimensional warped wavelet packet transform, with a binary tree where each node performs a one-dimensional inverse warped subband processing along a particular dimension d, with 1≦d≦n; and computing said digital output signal from said warped signal with an inverse signal warping. The sampling grid of the output signal is identical to the sampling grid of the input signal.

As opposed to prior art wavelet packet processors, as in R. Coifman Y. Meyer, M. Wickerhauser, “Method and apparatus for encoding and decoding using wavelet-packets”, U.S. Pat. No. 5,526,299, or to J. Li, S. M. Lei, “Arbitrary shape wavelet transform with phase alignment”, U.S. Pat. No. 6,233,357, or to A. Mertirs, “Image compression via edge-based wavelet transform,” Opt. Eng., vol. 38, no. 6, pp. 991-1000, 1999, the subband furthering is not performed on the input signal sampling grid, but on different sampling grids, called warping grids, that are typically adapted to the geometrical signal properties in different regions. As opposed to the subband filtering used in the wavelet packet procedures referenced above, a warped subband filtering is not computed with a convolution operator but with space varying filters, in order to handle the nonuniform structure of the warping grids. For three-dimensional video image sequences, as opposed to 3D-ESCOT method in J. Xu, Z. Xiong, S. Li and Y-Q. Zhang, “Three-Dimensional Embedded Subband Coding with Optimized Truncation (3-D ESCOT)”, Applied and Computational-Harrnonic Analysis 10, 290-315 (2001), the warping grid regions are not reduced to motion threads in time, and the warped subband processing satisfies a phase alignment constraint coherent with the warping grids, which is not satisfied by the 3D-ESCOT method. With this phase alignment property, warped wavelet packet coefficients have the same geometrical regularity as the input signal. This is particularly interesting when a bandeletisation module is located dowust of the warped wavelet packet processor.

To adapt the signal representation to the geometrical signal structures, the invention comprises a geometrical sampling module that computes said signal warping grids from a set of parameters called a warping geometry. The warping grids are typically computed to follow the directions in which the signal has regular geometrical variations. In an exemplary implementation, the warping geometry includes region parameters that specify a partition of the signal support into several regions, and includes deformation parameters that define a geometrical deformation function which specify the position of sampling points in each of said region. The signal support is divided into regions in which the signal has typically uniform geometrical properties so that one can adapt the warping grid to all structure in the region. For signals that are nearly periodic, the warping grid can adapt the sampling interval to the varying period, to obtain a nearly exactly periodic signal, which is taken advantage of, by the subsequent bandeletisation module.

Warped wavelet packet coefficients are computed with warping grids that typically follow directions in which the signals has regular variation& Because of the phase alignment property of warped subband processors, warped wavelet packet coefficients inherit the regularity of the signal in these directions. The invention preferably includes a bandeletisation module that yields a sparse representation by decorrelating said warped wavelet packet coefficients, by applying invertible one-dimensional linear operators along selected directions of said warped wavelet packet coefficients. For regions in which the signal is nearly periodic along particular directions, the bandeletisation module performs a periodic decorrelation, that takes advantage tage of the redundancy introduced by the existence of a quasi-periodicity. The resulting bandelet coefficients are decomposition coefficients in a basis composed of warped bandelet vectors. The one-dimensional linear decorrelation operators, can be chosen to be a cosine transform or a one-dimensional wavelet packet transform or a warped wavelet packet transform. The n-dimensional signal is then represented by its bandelet coefficients and the parameters of the warping geometry that specify the warping grids in each signal region.

The invention also includes an inverse bandeletisation module that computes an n-dimensional warped wavelet packet transform from a warping geometry and bandelet coefficient It comprises the steps of computing wavelet packet warping grids from said warping geometry, and computing warped wavelet packet coefficients by applying inverse one-dimensional linear operators along selected directions on said bandelet coefficients.

As opposed to the method E. Le Pernec and S. Mallat, in “Method and apparatus for processing or compressing n-dimensional signals by foveal filtering along trajectories”, U.S. patent application Ser. No. 09/834,587, filed Apr. 13, 2001, and in E. Le Pennec, S. Mallat, “Image Compression with Geometrical Wavelets”, Proceedings of International Conf. on Image Processing, Vancouver, September 2000, bandelet coefficients are not computed from foveal coefficients but from warped wavelet packet coefficients. As a consequence, no residual signal is needed to reconstruct a precise signal approximation from bandelets of warped wavelets as opposed to the above referenced method.

Signal processing procedures are efficiently implemented in a warped wavelet packet bandelet representation because of the ability to provide sparse and accurate representations by setting their smallest coefficients to zero. The invention comprises a processor compressing n-dimensional signals that quantizes the bandelet coefficients and encodes these quantized bandelet coefficients with the warping geometry to obtain a multiplexed data stream suitable for storage in a storage medium or for transmission over a transmission medium. The invention also comprises a processor that restores an input signal by applying a restoration processor on the bandelet coefficients and the warping-geometry to compute an output signal from these restored coefficients. The invention also comprises a processor that computes a signal feature vector from the warping geometry and bandelet coefficients, for pattern recognition applications including content based signal indexing or retrieval and signal matching.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects of this invention, the various features thereof as well as the invention itself may be more fully understood from the following description, when read together with the accompanying drawings in which:

FIG. 1 shows, in block diagram form, an exemplary embodiment of the invention which computes a warped wavelet packet bandelet representation of an input n-dimensional signal processes this representation and reconstructs an output n-dimensional signal

FIG. 2 shows, in block diagram form, an exemplary configuration of a warped wavelet packet bandelet processor.

FIG. 3 shows, in block diagram form, an exemplary configuration of an inverse warped wavelet packet bandelet processor.

FIG. 4 illustrates the partition of an image support in dyadic square regions and its quad-tree representation.

FIG. 5 shows, in block diagram form, an exemplary configuration of a warped wavelet packet binary tree that implements a warped wavelet packet processor.

FIG. 6 shows, in block diagram form, a warped wavelet packet binary tree that implements a warped wavelet processor for n=2 dimensional signals, over 3 levels.

FIG. 7 shows, in block diagram form, a warped wavelet packet binary tree that implements a warped wavelet processor for n=3 dimensional signals, over 2 levels.

FIG. 8 shows, in block diagram form, an exemplary configuration of a Warped Subband Processor (WSP) along a direction d.

FIG. 9 shows, in block diagram form an exemplary configuration of an Inverse Warped Subband Processor (MWSP) along direction d.

FIG. 10 shows, in block diagram form, an exemplary configuration of an inverse warped wavelet packet processor defined over the same binary tree as in FIG. 5.

FIG. 11 shows, in block diagram form, an exemplary configuration of wavelet packet warping grid subsampling along the same binary tree as in FIG. 5.

FIG. 12 shows, in block diagram form, an exemplary configuration of a bandeletisation processor.

FIG. 13 shows, in block diagram form, an exemplary configuration of an inverse bandeletisation processor.

FIG. 14 shows, in block diagram form, an exemplary configuration of a processor unit for signal compression.

FIG. 15 shows, in block diagram from, an exemplary configuration of a processor unit for signal decompression.

FIG. 16 shows, in block diagram form, an exemplary configuration of a processor unit for signal feature extraction.

FIG. 17 shows, in block diagram form, an exemplary configuration of a processor unit for signal feature extraction computed from compressed data.

DETAILED DESCRIPTION

FIG. 1 shows a system exemplifying the present invention. The system takes in input an n-dimensional digitized signal, where n is a positive integer. The digitized signal is given by an n-dimensional array of sample values. Electrocariograms (ECGs) are examples of a 1-dimensional signal, images are examples of 2-dimensional signals, and video image sequences are examples of 3-dimensional signals. The warped wavelet packet bandelet processor (101) computes from an input signal a warping geometry adapted to the input signal, as well as bandelet coefficients that are inner products with a family of bandelet vectors adapted to the signal geometry. The processor (102) tansforms the bandelet coefficients and the warping geometry according to a particular application such as signal restoration. In a transmission system, the processor may compress and code the warping geometry together with the bandelet coefficients into a bitstream, which is transmitted along a transmission medium, and decoded, to output processed bandelet coefficients and a processed geometry. The inverse warped wavelet packet bandelet processor (103) takes in input the processed warping geometry and the processed bandelet coefficients and computes an output signal.

FIG. 2 shows, in block diagram form, an exemplary configuration of the warped wavelet packet bandelet processor (101). A geometrical segmentation module (201) computes from the input signal a warping geometry which includes parameters from which the geometrical sampling module (202) computes a list of signal warping grids. A signal warping grid is an array of points that are distributed according to the geometrical signal properties in a given region. The signal warping processor (203) computes from the input signal and the signal warping grids a list of warped coefficients that stores the signal values at the locations of all points of all warping grids. The output warped signal is composed of the warped coefficients together with the signal warping grids. The warped wavelet packet processor (204) computes a warped wavelet packet transform that includes warped wavelet packet coefficients and wavelet packet warping grids. The bandeletisation processor (205) decorrelates the input warped wavelet packet coefficients by applying one-dimensional linear invertible operators along selected directions of the warped wavelet packet coefficient arrays. It outputs bandelet coefficients.

FIG. 3 shows, in block diagram form, an exemplary configuration of the inverse warped wavelet packet bandelet processor (103). The geometrical sampling (301) computes signal warping grids from an input warping geometry. The wavelet packet warping grid subsampling (302) computes wavelet packet warping grids from the signal warping grids. The inverse bandeletisation (303) implements the inverse of the bandeletisation (205). It inputs the wavelet packet warping grids and bandelet coefficients and outputs a warped wavelet packet transform including warped wavelet packet coefficients and the corresponding wavelet packet warping grids. The inverse warped wavelet packet processing applied by processor (304) implements the inverse of the warped wavelet packet processor (204): from the input wavelet packet transform, it computes a warped signal including warped coefficients and the signal warping grids. The inverse signal warping (305) recovers an output signal defined over the same sampling grid as the input signal in FIG. 2, with an interpolation procedure.

In an exemplary embodiment of the present invention, each process can be carried out by a computer program. The computer program is run on a computing device which may comprise a storage device such as a magnetic disk or an optical disk on which the n-dimensional digital input signal may be stored. The n-dimensional digital input signal may also have been received by the computing device after a transmission via a network connection such as an ethernet link, a phone line, a wireless transmission, etc . . . . The computer program can be stored on the storage device or on a Read Only Memory (ROM). Each process is executed by a Central Processing Unit (CPU) coupled to a Random Access Memory (RAM). The output of each process can be stored on the storage device or transmitted via a network connection. In another exemplary embodiment, each process can be carried out by a dedicated electronic device comprising the instructions to implement said process

Warping Grids and Warping Geometry

The input n-dimensional discrete signal in FIG. 2 is specified by an array of samples S(m) where m=(m₁, . . . , m_(n)) is an n-dimensional index parameter and each m_(d) is an integer for 1≦d≦n. The position of these samples is represented by the point of coordinates m=(m₁, . . . , m_(n)) in R^(n). The signal support S_(s) in R^(n) is the halo of all m εZ^(n) for which the signal value is defined. The halo(M) of a subset M of Z^(n) is the subset of R^(n) defined by a union of hypercubes:

${{halo}(\mathcal{M})} = {\bigcup\limits_{m\;\varepsilon\;\mathcal{M}}{\left\lbrack {{m_{1} - {1/2}},{m_{1} + {1/2}}} \right\rbrack \times \ldots \times \left\lbrack {{m_{n} - {1/2}},{m_{n} + {1/2}}} \right\rbrack}}$

To manipulate multidimensional indexes of arrays, the following notations are used. For any integer a and any positive integer b, there exists a unique pair (q, r) of a quotient and a remainder such that a=bq+r where r ε{0 . . . , b−1} (Euclidean division). We define the ‘mod’ and ‘div’ operators by a mod b=r and a div b=q. For any k=(k₁, . . . ,k_(n))ε Z^(n), we write: k mod_(d) 2=k_(d) mod 2 k div_(d) 2=(k ₁ , . . . , k _(d−1) , k _(d) div 2,k _(d+1) , . . . , k _(n)) 2_(d) k=(k ₁ , . . . , k _(d−1), 2k _(d) , k _(d+1) , . . . , k _(n) ) k/2_(d)=(k ₁ , . . . , k _(d−1) , k _(d)/2, k _(d+1) , . . . k _(n)) and 1_(d)=(0, . . . 0, 1, 0, . . . , 0) is a unit displacement along direction d: the coordinate d is equal to 1 and all others are equal to 0. For any x ε R^(n) and r ε (0, +∞) we also write

$\begin{matrix} {{{x}r} = {{\left( {\sum\limits_{d = 1}^{n}{x_{d}}^{r}} \right)^{1/r}\mspace{14mu}{for}\mspace{14mu} r} < \infty}} \\ {{{x}\infty} = {\max\limits_{d = {1\ldots\mspace{14mu} n}}\mspace{14mu}{x_{d}}}} \end{matrix}.$ When x and u are two vectors of R^(m), for some m>0, we write x.u=Σ_(d=1) ^(m) x_(d) u_(d) the inner product between two vectors x and u. When A is a matrix of any size of entries a_(ij), A^(T) is the conjugate transpose matrix of entries a_(ji)*, and A^(−T) is a shorter notation for (A^(T))⁻¹.

The warped wavelet packet transform is computed along a union of sampling grids called warping grids. The union of the warping grids is typically not equal to the signal sampling grid and is adapted to the geometrical structure of the signal. In an exemplary embodiment, each warping grid is represented by an array of points WG(i, k) ε R^(n) where i is a fixed index and k ε Z^(n) is a position index For a fixed region index i, the warping grid WG(i, k) is only defined for a subset of values of k, and corresponds to a region R_(i) of the input signal support, over which the input signal has a homogeneous geometrical structure. Any other state of the art representation of arrays of sampling points may be used. The warping geometry includes parameters calculated from the n-dimensional input signal by the geometrical segmentation module (201), that characterize the warping grids which are computed by the geometrical sampling module (202) from the warping grids.

In an exemplary implementation, the warping geometry specifies a partition of S_(s) into regions R_(i) ⊂R^(n), with S_(s)=∪_(i)R_(i), and specifies a geometrical deformation δ_(i)(k)=(δ_(i,1)(k), . . . , δ_(i,n)(k))εR^(n) whose support is the set of k εZ^(n) such that δ₁(k) εR_(i). For each region R_(i), the geometrical sampling module (202) computes a signal warping grid

$\begin{matrix} {{{WG}\left( {i,k} \right)} = \left\{ {\begin{matrix} {\delta_{i}(k)} & {{{if}\mspace{20mu}{\delta_{i}(k)}} \in R_{i}} \\ {nil} & {otherwise} \end{matrix}.} \right.} & (1) \end{matrix}$ The warping geometry also specifies a regularity descriptor RegWG(i) which is a vector (p₁, . . . , p_(n)) that characterizes the regularity of the signal samples along the different directions of the warping grid WG(i, k). Along each direction d, if p_(d)=0 then for k fixed, WC(i, k+l1_(d)) can have irregular variations as a function of l. If pd_(d)≠0 then WG(i, k+l p_(d)1_(d)) has regular variations as a function of l. Thus, p_(d)=1 indicates that the function WC(i, k+l 1_(d)) of l is slowly varying, whereas p_(d)>1 indicates that the function WC(i, k+l1_(d)) of l is nearly periodic of period p_(d). The vector RegWG is stored together with the warping grids WG and is used by the bandeletisation processor (205) to perform an appropriate decorrelation of warped wavelet packet coefficients. The geometrical segmentation module represents the partition into regions R_(i) with region parameters, the geometrical deformations δ_(i)(k) with deformation parameters, and the vector RegWG(i) with vector parameters, from which R_(i), δ_(i)(k) and ReWG(i) can be recovered. To compute the warping grids with (1), the geometrical sampling module (202) first recovers for each i, R_(i), δ₁(k) and RegWG(i) by inverting these operators. The following gives exemplary embodiments to compute he warping geometry from an n-dimensional signal These embodiments are not limitative and any state of the art technique may be used to compute a warping geometry from which one can compute warping grids.

In an exemplary embodiment, each region R_(i) is the halo in R^(n) of R_(i) ∩Z^(n), and is s ed by a binarymembership map for all k εZ^(n) b_(i)(k)=1 if k εR_(i) and b_(i)(k)=0 otherwise Any state of the art parameterization technique may be used to represent these binary maps, including a chain coding of their boundary. In yet another exemplary embodiment, a segmentation is calculated with a parameterized modification such as a linear warping in R^(n) of a predefined segmentation, in order to adapt the segmentation for the signal. The region parameters then include only the modification parameters such as the parameters of the linear warping operator. For images of human faces, a state of the art technique may be used to detect features such as the eyes. We then compute the affine warping which renormatizes the position of these features and use this affine warping to adapt a predefined segmentation of the fine into regions adapted to the geometrical face struces. In yet another exemplary embodiment of the geometrical segmentation (201), each region R_(i) is a pammetrized geometrical shape such as a hyperrectangle or an ellipsoid in R^(n) in which case the deformation parameters can include their center (c₁, . . . , c_(n)), and their widths (w₁, . . . , W_(n)) along each of the n direction, or R_(i) may be a union of such geometrical shapes.

In an exemplary embodiment, the signal support is divided into hyperrectangle regions R_(i) that are dyadic hypercubes of width equal to w 2^(j) with j≦0, where w is a fixed integer parameter. In an exemplary embodiment, this partition is computed with averaged orientation vector with a splitting procedure. The signal gradient is a vector ∇S(m)=(Δ₁S(m₁,m₂), . . . , Δ_(n)S(m₁, m₂)) computed at each point m ε Z^(n) ∩S_(s) with: Δ_(d) S(m)=S(m)−S(m−1_(d)) for 1≦d≦n. For any u ε R^(n) we define the average gradient energy over a region M, in the direction of u by:

$\begin{matrix} {{C_{\mathcal{M}}(u)} = {\sum\limits_{m\;{\varepsilon\mathcal{M}}}{{{{\nabla{S(m)}} \cdot u}}^{2}.}}} & (2) \end{matrix}$ The average orientation is defined as the unit vector in R^(n) that minimizes C(u):

$u_{\mathcal{M}} = {\arg\mspace{14mu}{\min\limits_{{u} = 1}{{C(u)}.}}}$ We calculate the n by n matrix Q _(M) whose coeficients are the averages for m ε M of the coefficients of the n by n matrix Q_(M)(m)=∇S(m) ∇S(m)^(T). Since C(u)=u^(T) Q _(M) u, the vector u_(M) is a unit norm eigenvector of Q _(M) corresponding to the minimum cigenvalue which is equal to u_(M) ^(T) Q _(M)u_(M)=C_(M)(u_(M)). This value measures the signal regularity in the most regular direction over M.

The receive splitting procedure Split(R) is defined over any hypercube R and initially applied to the whole signal support R₁=S_(s). For any i, Split(R_(i)) computes the average orientation u_(i) in M_(i)=R_(i) ∩Z^(n). Let T₁ be a parameter and C_(i)=u_(i) ^(T) Q _(Mi)u_(i). If C_(i)>T₁ then the square region R_(i) is divided into a partition of 2^(n) hypercubes of twice smaller width R₂ _(n) _(i), R₂ _(n) ₁₊. . . , and R_(n) _(n) _(i+2) _(n) ⁻¹, and Split(R_(i)) recursively computes Split(R₂ _(n) _(i)) . . . Split(R₂ _(n) _(i+2) _(n) _(−1). If C) ₁≦T₁ then Split(R₁) returns the region index i, which is the index of the leaf corresponding to R_(i) in a tree representation of the partition. In this tree, called 2^(n)-tree, any inside node has 2^(n) children. The region parameters of the warping geometry parameterizes the tree corresponding to the signal support segmentation with a state of the art representation of a 2^(n)-tree. In yet another exemplary embodiment, the region partition is further modified with a merging procedure that yield a segmentation into regions that are unions of hypercubes. Adjacent hypercube regions are merged using a criterion similar to the splitting criterion above: whenever the minimum eigenvalue of Q _(M) is smaller than alfixed threshold T₂, where M is the union of two region grids M_(i) ₁ =R_(i) ₁ ∩Z^(n) and M_(i) ₂ =R_(i) ₂ ∩Z^(n), the two adjacent regions i₁ and i₂ are merged. The region parameters then includes parameters specifying this merging. In dimension n=2, the partition is represented by a quad-tree, as illustrated in FIG. 4. The square image support is subdivided into four equal square regions. The lower left square is not subdivided and corresponds to the leaf 4 of the quad-tree above. The upper left square is subdivided in four squares corresponding to the leaves 20, 21, 22, 23 of the quad-tree. In an example of a region merging procedure, regions 20 and 21 are merged together into a single region, and regions 6, 30 and 126 are also merged together.

In dimension n=3, in yet another exemplary embodiment of the geometrical segmentation (201), the regions R_(i) are 3-dimensional rectangles, of fixed width w along one of the direction, say x_(s), and whose projections on the plane (x₁, x₂) are dyadic squares that define a partition of the signal support, which is represented by a quad-tree. This quad-tree can be calculated with a splitting procedure similar to the quad-tree splitting procedure for n=2, using average gradient energy measurements over each of the 3-dimensional rectangles specified by a square in the plane (x₁, x₂) and a width w along x₃.

Directional geometrical deformations are exemplary embodiments of geometrical deformations δ_(i)(k)=(δ_(i,1)(k), . . . δ_(i,n)(k)) that can have n+1 different directions. The direction 0 corresponds to a grid parallel to Z^(n) for which δ_(i)(k)=k+c_(i), where c_(i) is a constant vector. For any 1≧d≧n, a geometrical deformation along direction d satisfies δ_(i,l)(k)=k_(l)+C_(i,l), for l≠d and δ_(i,d)(k)=k_(d)+c_(i,d)(k₁, . . . , k_(d−1), k_(d+1), k_(n)). In an exemplary embodiment, the constants c_(i,l) for l≠d are chosen to depend only upon the geometry of each region R_(i) and their value do not need to be stored. It may be minus the minimum coordinate in the direction l among all points in R_(i). Any state of the art parameterization technique may be used to define the deformation paramneters that specify the function c_(i,d). In an exemplary embodiment, deformation parameters are decomposition coefficients of c_(i,d)(k₁, . . . , k_(d−1), k_(d+1), k_(n)) in a basis such as a wavelet basis or a block cosine basis defined over the set of parameter indexes (k₁. . . , k_(d−1), k_(d+1), . . . , k_(n)) from which there exists k_(d) such that (k₁+c_(i,1), . . . , k_(d−1)+c_(i,d−1), k_(d), k_(d+1)+c_(i,d+1), . . . , k_(n)+c_(i,n)) ε R_(i). Fast transforms as described in S. Mallat, “A wavelet tour of signal processing”, Academic Press, 1998, can be used to compute these decomposition coefficients.

In an exemplary embodiment, for each regions R_(i) of a signal partition, the deforamion direction is computed by finding the direction in which the signal is the most irregular. It is done by calculating the matrix Q _(R) _(i) previously defined. If the trace of Q _(R) _(i) , is smaller than an adjustable parameter T₂ then the region is uniformly regular, in which case the geometrical deformation has the direction 0 and RegWG(i)=(1,1, . . . , 1). Otherwise the direction in which the signal is the most irregular is defined as the diction d such that.

${1_{d}^{T}{\overset{\_}{Q}}_{\mathcal{R}_{i}}1_{d}} = {\max\limits_{1 \leq {/{\leq n}}}{1_{l}^{T}{\overset{\_}{Q}}_{\mathcal{R}_{i}}{1_{i}.}}}$ The regularity descriptor RegWG(i)=(p₁,p₂, . . . p_(n)) is then defined by p_(l)=1 for each l≠d and p_(d)=0. We shall see that the value p_(d)=0 can later be updated if there exist quasi-periodic variations along the direction d.

In dimension n=2, a directional geometrical deformation in the direction 1 can be written δ_(i)(k)=(k ₁ +c _(i,1)(k ₂), k ₂ +c _(i,2)) and in the direction 2 δ_(i)(k)=(k ₁ +c _(i,1) , k ₂ +c _(i,2)(k ₁)). In an exemplary embodiment, the geometrical deformation over each region is also calculated from average orientation vectors. Let us consider a region whose deformation direction is d=2, the other case d=1 being treated similarly. For each l ε Z let R_(i,l) be the set of integers m₂ such that (l, m₂) ε R_(i). The first parameter of the geometrical deformation c_(i,1) is an integer that is set to 0. To compute c_(i,2)(k₁), we compute the average orientation vector u_(i)(l)=(u_(i,1)(l),u_(i,2)(l)) over R_(i,l). Let a be the minimum of all integers l such that R_(i,l)≠∅. We define

${{\overset{\_}{c}}_{i,2}\left( m_{1} \right)} = {\sum\limits_{l = a}^{m_{1}}{\frac{u_{i,2}(l)}{u_{i,1}(l)}.}}$ The discrete signal c _(i,2) (m₁) is then decomposed over a basis such as a discrete wavelet basis or a discrete cosine basis with a linear operator. To obtain a sparse paraneterization, in an exemplary embodiment, all decomposition coefficients of c _(i,2)(m₁) of amplitude below a threshold T_(s) are set to zero. For compression applications, in yet another exemplary embodiment, the decomposition coefficients of c _(i,2)(m₁) are quantized by a scalar quantization. We define c_(i,2)(m₁) as the signal reconstructed by applying the inverse linear operator on the thresholded or quantized coefficients of c _(i,2)(m₁). The deformation parameters of the warping geometry spedify the direction d=2 and the tholded or quantized decomposition coefficients used to compute c_(i,2)(k₁). An equivalent procedure computes the geometrical deformation in the direction d=1.

In yet another exemplary embodiment, in any region R_(i) ⊂R^(n), a directional geometrical deformation is computed in dimension n in a direction 1≦d≦n by detecting local maxima of the gradient. The signal gradient ∇S(k) is calculated at each k ε Z^(n) ∩S_(s). For any p≠d we set δ_(i,p)(k)=k_(p). For any fixed (k₁, . . . k_(d−1), k_(d+1), . . k_(n)) we consider the set of all l such that (k₁, . . . k_(d−1), l, k_(d+1) . . . , k_(n)) ε R_(i) and if this set is not empty, we find in this set the integer k_(d) such that |∇S(k)|² is maximum in k=(k₁, . . . , k_(d−1), k_(d), k_(d+1) . . . . , k_(n)) and define c _(i,d)(k ₁ , . . . , k _(d−1) , k _(d+1) , . . . k _(n))=k _(d). We decompose c _(i,d) in a discrete basis such as a wavelet basis in dimension n−1 and threshold its coefficients or quantize its coefficients and denote by c_(i,d)(k₁, . . . , k_(d−1), k_(d+1), . . . , k_(n)) the hypersurface reconstructed from the thresholded or quantized coefficients of c _(i,d). The deformation parameters specify the direction d and the thresholded or quantized decomposition coefficients used to compute c_(i,2)(k₁).

For an input video image sequence, which is an n=3 dimensional signal, in yet another exemplary embodiment, the warping grid segmentation module (201) computes a directional geometric deformation by applying a time displacement to a 2-dimensional geometrical deformation using estimated motion vectors. The video sequence is written S(m₁,m₂, m₃) where m₃ is the time parameter, and for a fixed m_(s)=l, S(m₁, m₂, l) is an image. For any region R_(i) and m₃=l fixed, let R_(i,l) be the image region of all (m₁, m₂) such that (m₁,m₂,l) ε R_(i). To a three-dimensional region R_(i), we associate a spatial geometrical deformation (c_(i,1)i(k₁, k₂), C_(i,2)(k₁, k₂)), and a two-dimensional regularity descriptor (p₁p₂) which indicates the spatial direction in which the signal is regular. Let a be the minimum l for which R_(i,l)≠∅. In an exemplary embodiment, the said spatial geometrical deformation and two dimensional regularity descriptor (p₁, p₂) are provided by computing a two-dimensional directional geometrical deformation in the slice R_(i,a). We define RegWG=(p₁,p₂, 1) which indicates that the variations in time are regular. For any l for which R_(i,1) is not empty, an average motion vector is computed over R_(i,l) by calculating the average displacement of the signal components in this region with respect to their position in the previous image S(m₁, m₂, l−1). Any state of the art motion estimation algorithm may be used, including matching procedures or gradient based computations. Let v_(i)(l)=(v_(i,1)(l), v_(i,2) (l)) be the average motion vector of the region R_(i,l) and

$\left( {{{\overset{\sim}{w}}_{i,1}\left( m_{3} \right)},{{\overset{\sim}{w}}_{i,2}\left( m_{3} \right)}} \right) = {\sum\limits_{l = a}^{m_{s}}{{v_{i}(l)}.}}$ In an exemplary embodiment, a sparse parameterization is obtained by decomposing this displacement vector in a basis, thresholding or quantizing the decomposition coefficients and reconstructing an approximate displacement vector (w_(i,1)(m₃),w_(i,2)(m₃)) from the quantized or thresholded coefficients. Deformation parameters include these thresholded or quantized decomposition coefficients. The motion geometric deformatin is then defied by δ_(i)(k)=(c _(i,1)(k ₁ , k ₂)+w _(i,1)(k ₃), c _(i,2)(k ₁ , k ₂)+w _(i,2)(k ₃), k ₃).

The geometrical segmentation module (201) can also detect the existence of quasi-periodic signal variations, measures these quasi-periods to adapt the warping grid in each region. We shall first consider the case of an input one-dimensional signal and then describe an exemplary embodiment for n-dimensional signals.

For a one-dimensional signal S(m), the geometrical segmentation processor divides the signal support S_(s) into intervals [a_(i), a_(i+1)] in which either the signal is quasi-periodic, with quasi-periods that have a bounded relative variation, or in which the signal has no quasi-periodicity. The value of RegWG(i) is used to indicate what is the nature of the signal in each interval. RegWG(i)=0 indicates that S is not quasiperiodic over [a_(i), a_(i+1)−1], in which case the geometric deformation mapping is simply δ_(i)(k)=k+c_(i) for some integer c_(i). RegWG(i)=2^(l) for some integer l indicates that S is quasi-periodic inside [a_(i), a_(i+1)−1] the varying period being close to 2^(l). In a particular embodiment, the time-varying period in [a_(i), a_(i+1)−1] is defined from J_(i) consecutive period measurements P_(i)(j) for 1≦j≦J_(i) with Σ_(j=1) ^(J) ^(i) P_(i)(j)=a_(i+1)−1−a_(i). To obtain a warping grid WG(i, k) such that the corresponding warped signal WC(i, k) has a quasi-period equal to 2^(l), the geometrical deformation δ_(i)(k) is chosen to be a mapping from an interval [0, J_(i)2^(l)] to [a_(i), a_(i+1)−1] that maps 0 to a_(i) and each j2¹ to a_(i)+P_(i)(1) + . . . +P_(i)(j). Let b₀=_(i) and b_(j)=a_(i)+P_(i)(1) + . . . +P_(i)(j) for all j in {1, . . . , J_(i)}. We set δ_(i)(j2^(l))=b_(j) for j=0, . . . , J_(i) and intermdiate values δ_(i)(k) for k≠j2^(l) are calculated with any state of the art interpolation procedure such as the piecewise linear one: ∀kε[j2^(l)+1, (j+1)2^(l)−1], δ_(i)(k)=b _(j)+(b _(j+1) −b _(j))×(2^(−l) k−j)

Any state of the art periodic detection procedure may be used to segment the signal support and compute quasi-periods P_(i)(j) in each interval. In an exenrplary embodiment, the periodicity is measured with a variogmm, defined over [0, L] and for any (m,p) εZ²:

$\begin{matrix} {{V\left( {m,p} \right)} = {\sum\limits_{l = 0}^{L}{{{{S\left( {m + l} \right)} - {S\left( {m + p + l} \right)}}}.}}} & (3) \end{matrix}$ Period values are estimated in a predefined range [P_(min), P_(max)]. An admissible period computed from reference point m is defined as the minimum p>P_(min) with p<P_(max) such that V(m, p) <T where T is a fixed threshold If such a p does not exist then S(m) is considered as non-periodic on [m, m+P_(max)].

We suppose that the signal support S_(s) is an interval and cut iteratively this interval into regions [a_(i), a_(i+1)−1], by constructing iteratively the sequence (a_(i))_(i). The sequence is initialized by setting a₁ to be the first point of S_(s). At each iteration, a value a_(i) has been determined and we compute the next value a_(i+1) as follows.

-   -   If the variogmam (3) indicates that S is not periodic on [a_(i),         a_(i)+P_(max)], i.e. there is no admissible period from         reference point m=a_(i), then RegWG(i)=0. We find the minimum         integer q for which when m=a_(i)+q P_(max)+1 the variogram (3)         finds an admissible period p from reference point m=a_(i)+q         P_(max)+1 and we set a_(i+1)=m−1.     -   If the variogram (3) finds an admissible period p computed from         reference point m=a_(i) then we set ReWG(i)=2^(l) where l is the         closest integer to log₂(p). We set P _(i)(1)=p, b₀=a_(i),         b₁=a_(i)+ P _(i)(1) and compute iteratively the next values of         b_(q), for q>1 as follows. We suppose that b_(q) has been         computed. Every time the variogram (3) finds an admissible         period p from reference point m=b_(q), and if max(2^(l),         p)/min(2^(l),p) <T₄ where T₄ is a fixed threshold then we set P         _(i)(q+1)=p and b_(q+1)=b_(q)+p. This is continued until the         variogram (3) does not find an admission period p from some         reference point m=b_(J) _(i) , or if it finds a period p such         that max(2^(l), p)/min(2^(l), p)≧T₄. The quasi-period sequence P         _(i)(j) for 1≦k≦K_(i) is considered as a signal that decomposed         in a basis and whose decomposition coefficients are thresholded         or quantize We write P_(i)(j) the sequence reconstructed from         these thresholded or quantized decomposition coefficients. We         then set a_(i+1)=a_(i)+Σ_(j=1) ^(J) ^(i) P_(i)(j).         This progressive construction of intervals [a_(i), a_(i+1)−1]         continues until we reach the end of the support S_(s). In an         exemplary embodiment, the region and deformation parameters of         the warping geometry specify for each interval the size 2^(l),         the number of quasi-periods J_(i), and the thresholded or         quantized decomposition coefficients used to compute the         quasi-period sequence P_(i)(j). When the signal is not periodic         in [a_(i), a_(i+1)−], the length P_(i)(1)=a_(i+1)−a_(i) is         stored. Since a_(i+1)=a_(i)+Σ_(j=1) ^(J) ^(i) P_(i)(j) the         interval boundaries are recovered from these parameters.

We now consider for n>1 an n-dimensional signal S(m) whose support S_(s) has been partitioned into regions R_(i) over which a geometrical deformation δ_(i)(k) was calculated together with RegWG(i)=(p₁, . . . , p_(n)) with p_(l)=1 or p_(l)=0. Several exemplary embodiments were described to compute such a partition. When the correlation type RegWG(i) of a region i is such that P_(d)=0 for a single direction d, an exemplary embodiment of the geometrical segmentation module (201) scans the signal in region i along the direction d for which p_(d)=0, and if there exists some quasi-periodicity along the samples of the warping grid defined by δ_(i)(k), it further divides R_(i) according to these quasi-periodicity. The quasi-periodicity is measured with a variogram in the direction d, with a similar strategy as the exemplary embodiment described for n=1 dimensional signals. This leads to new sub-regions R_(i,j) inside each of which a new geometric deformation further modifies the geometrical deformation δ_(i)(k) so that the resulting warping grid has a quasi-periodicity of fixed size 2^(l) along the direction d, and we replace the d-th component of RegWG(i,j)=(p₁, . . . , p_(n)) with p_(d)=2^(l). When the number of directions d such that P_(d)=0 is higher than 1, a similar periodicity scanning and segmentation is performed, wherein the periodicity scanning from a reference point consists in finding a family of independent period vectors by minimizing a variogram function, and wherein the periodicity scanning is performed along the samples of the warping grid defined by δ_(i)(k) along the axes for which P_(d)=0. After proper modification of the geometrical deformation function δ_(i)(i, k), the signal is quasi-periodic of period 2^(l) _(d) along some directions d of the warping grid defined by δ_(i)(i, k), and for these directions the vector RegWG(i) is updated by replacing its d-th coordinate previously equal to0with 2^(l) ^(d) .

Signal Warping and Inverse Warping

The signal warping processor (203) computes a value WC(i, k) of the signal at each warping grid points WG(i, k)≠nil, given the input signal sample values S(m). This calculation can be performed with any state of the art interpolation procedure. The output warped signal includes the warped coefficients WC(i, k) together with the signal warping grid WG(i, k).

An exemplary embodiment is obtained by computing the interpolation with an interpolation function φ(x) for x ε R^(n) which satisfies φ(0)=1 and φ(m)=0 for m ε Z^(n) and m≠0. At any location WG(i, k)=x≠nil the interpolated coefficients are then

$\begin{matrix} {{{WC}\left( {i,k} \right)} = {\sum\limits_{m\; \in {S_{s}\bigcap Z^{n}}}{{S(m)}{{\phi\left( {x - m} \right)}.}}}} & (4) \end{matrix}$ An example of an interpolation function φ(x) is a separable function φ(x ₁ , . . . , x _(n))=φ₀(x ₁) . . . φ₀(x _(n)) where φ₀(t) is an interpolation function with φ₀(0)=1 and φ₀(m)=0 for m ε Z and m≠0. In an exemplary embodiment, an interpolation procedure such as described in P. Thevena; T. Bhl, M. Unser, “Interpolation Revisited,” IEEE Transactions on Medical Imaging, vol. 19, no. 7, pp. 739-758, July 2000, is used to compute WC(i, k). State of the art techniques are used to adapt the interpolation kernel at the boundary of S_(s).

The inverse signal warping processor (305) in FIG. 3 takes in input a warped signal including the signal warping grids WG₁ and warped coefficients WC₁ at the corresponding locations, and computes the output signal that is written S_(o)(m) defined for all m ε Z^(n) in the support S_(s) of the input signal in FIG. 2. The operation it performs is an approximation of the inverse of the interpolation implemented by the signal warping processor (203). The output signal can be calculated with any state of the art interpolation procedure. An exemplary embodiment of this interpolation is calculated as follows. For each m ε Z^(n) ∩S_(s), we find the warping grid point WG(i_(m), k_(m))=x that has a minimum distance d(x, m) where d may be any distance such as the Euclidean distance. The value S_(o)(m) is computed by finding local coordinates c of m in the warping grid WG(i, k) and performing an interpolation with a uniform metric in this warping grid. In an exemplary embodiment, for each direction 1≦d≦n, we find among the two neighbors WG(i_(m), k_(m)±1_(d)) the one WG(i_(m),k_(m)+ε_(d)1_(d)) with ε_(d)=1 or −1 that is the closest to m. The local coordinates c=(c₁, . . . , c_(n)) are then deed as the sums of the coordinates of k_(m) and the coordinates of the vector m−WG(i_(m), k_(m)) in the basis of the n vectors ε_(d)(WG(i_(m), k_(m)+ε_(d)1_(d))−WG(i_(m), k_(m))) for 1≦d≦n.

Given the local coordinate c of the point m ε Z^(n) in the warping grid WG(i_(m), k), in an exemplary embodiment, S_(o)(m) is computed with an interpolation function φ(x) which satisfies φ(0)=1 and φ(k)=0 if k ε Z^(n). The interpolated coefficients are then given by

${S_{o}(m)} = {\sum\limits_{k\; \in \; Z^{n}}{{{WC}\left( {i_{m,}k} \right)}{{\phi\left( {c - k} \right)}.}}}$ In a preferred embodiment, the interpolation function φ(x) is a separable function like in (5). State of the art techniques are used to adapt the interpolation kernel at the boundary of the warping grid WG(i_(m), k). Warped Wavelet Packet Processor and Inverse

The warped wavelet packet processor (204) computes a warped wavelet packet transform from the input warped signal, with a binary tree of one-dimensional warped subband processings. An exemplary embodiment of this binary tree is illustrated in FIG. 5. The input warped signal includes the warped coefficients WC₁ and the signal warping grids WG₁. Similarly to a tree-structured subband coder as in M. J. Smith and T. P. Bariwell III, “Exact reconstruction for tree-structured subband coders”, IEE Trans. Acoust., Speech and Signal Proc., vo. 34, no. 3, pp. 431-441, 1986, a warped wavelet packet transform is calculated with a binary tree of subband decompositions which splits the input signal into two signals which have the same total number of samples as the input signal, but in this case the subband decomposition is performed by a Warped Subband Processor (WSP) adapted to the warping grids that are typically different from the input signal sampling grid. This warped subband processor thus operates differently from a state of the art subband filtering procedure, as in S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 11, p. 674693, July 1989. Each node of the binary tree is labeled by an integer p. At the root p=1. The left and right children of a node p are respectively labeled by the integers 2p and 2p+1. Each Warped Subband Processor (WSP) in (501), (502), (503), (504) and (505) performs a one-dimensional warped subband processing along the lines of the input warped coefficients in a direction 1≦d≦n, which can be chosen arbitrarily for each node p of the binary tree. At a node p, this warped subband processing splits the input warping grids WG_(p) and the corresponding warped coefficients WC_(p) into two sets of warping grids WG_(2p) and WG_(2p+1), and two sets of warped coefficients WC_(2p) and WC_(2p+1) defined over these grids. The warped coefficients WC_(2p) are obtained with a warped subband filtering using a low-pass filter whereas the warped coefficients WC_(2p+1) are obtained with a high-pass filter. The output warped wavelet packet transform corresponds to the family of warped coefficients WC_(p) together with their warping grids WG_(p), at the nodes p ε P which are the leaves of the warped wavelet packet binary tree, In FIG. 5, it is composed of WC₄ and WG₄, WC₇ and WG₇, WC₁₀ and WG₁₀, WC₁₁ and WC₁₁, WC₁₂ and WG₁₂, WC₁₃ and WG₁₃, so P={4,7,10,11,12,13} in this case.

A warped wavelet processor is a particular embodiment of warped wavelet packet processor, corresponding to a specific binary tree. This binary tree is the same as the one used to compute a separable n-dimensional non-warped wavelet transform as in S. Mallat, “A wavelet tour of signal processing”, Academic Press, 1998. ID dimension n=2, the biay tree is illustrated in FIG. 6. This binary tree is composed of WSP (Warped Subband Processor) (601-609) in directions d_(p)=1 if 2^(k)≦p <2^(k+1) with k even, and d_(p)=2 if 2^(k)≦p<2^(k+1) with k odd. After the first two levels of WSP decompositions in the binary tree, only the warped coefficients WC₄ and warping grids WG₄ are further processed. The same succession of warped subband processors in the directions d=1 and d=2 is then applied and only the output of the low-pass branch corresponding to WC_(p) and WG_(p) for p=4^(k) is further sub-decomposed, as illustrated in FIG. 6. This binary tree of processors can be further continued on more levels.

In dimension n=3, the biary tree of a warped wavelet processor is illustrated in FIG. 7. This binary tree is composed of WSP (701-714) in directions d_(p)=1 if 2^(k)≦p<2^(k+1) with k=0 mod 3, d_(p)=2 if 2^(k)≦p<2^(k+1) with k=1 mod 3, d_(p)=3 if 2^(k)≦p<2^(k+1) with k=2 mod 3. After three levels of WSP decompositions only the warped coefficients WC_(p) and warping grids WG_(p) with p=8^(k), obtained with a succession of low-pass subband warped filtering, are further processed, like WC₈ and WG₈ in FIG. 7.

An exemplary configuration of a warped subband processor in a direction d is illustrated by the block diagram in FIG. 8. The grid subsampling along direction d performed by module (801) splits the warping grid WG_(p) into two warping grids WG_(2p) and WG_(2p+1), by defining for all i and all k WG _(2p)(i,k)=WG _(p)(i, 2_(d) k) and WG _(2p+1)(i, k)=WG _(p)(i, 2_(d) k+1_(d)), and RegWG _(2p)(i)=RegWG _(2p+1)(i)=RegWG _(p)(i)/2_(d). Note that in this last formula, the resulting vectors RegWG_(2p)(i) and RegWG_(2p+1)(i) may have non integer entries, like ½ or ¼. In the description of the present invention, warping grids WG_(p) corresponding to separate nodes of the wavelet packet binary tree are considered as stored in separate arrays, to simplify the explanations, but can also be stored in a single array.

If the warped wavelet packet transform is done individually within each grid, when reconstructing an output signal from processed warped coefficients, it creates discontinuities at the grid boundaries. To avoid this effect, the warped wavelet packet processor performs the subband filtering across warping grids. Grid connections are computed by the neighborhood connection processor (802), which establishes neighborhood connections between boundary points of different regions along the direction d. A warped subband filtering processor (803) along direction d takes in input warped wavelet coefficients WC_(p) with the corresponding warping grids WG_(p) and a neighborhood connection graph WN_(p)(i, k, Δ, d) along direction d and performs a subband filtering within each grid of coefficients WC_(p)(i, k) and across grids, with adapted filters. It outputs the subband warped coefficients WC_(2p) and WC_(2p+1).

The neighborhood connection processor (802) establishes a neighborhood relation which is symmetric, meaning that if a first point is a left neighbor of a second point then the second point is the right neighbor of the first point In an exemplary embodiment, for each WG_(p)(i, k)≠nil WN_(p)(i, k, −1, d)=(i_(l), k_(l)) gives the index i_(l) of the region where the left neighbor of WG_(p)(i, k) in the direction d is located and k_(l) is its index in this region. Similarly, WN_(p)(i,k,1,d)=(i_(r),k_(r)) gives the index i_(r) of the region where the right neighbor of (i, k) in the direction d is located and k_(r) is its index in this region. The computation of WN_(p)(i, k, ±1, d) proceeds as follows: if WG_(p)(i, k −1_(d))≠nil then WN_(p)(i, k, −1, d) is set equal to (i, k−1_(d)), and otherwise (i, k) is included in the Right list that stores the indexes of all points that do not have a left neighbor in the same region. Similarly, if WG_(p)(i, k+1_(d))≠nil then WN_(p)(i, k, 1, d) is set equal to (i, k+1_(d)), and otherwise (i, k) is included in the Left list that includes the indexes of all points that do not have a right neighbor in the same region.

For all (i, k) in the Right list we search for a left neighbor point (i′, k′) in the Left list, with i′≠i and which minimizes Cost(WG_(p)(i′, k′), WG_(p)(i, k)), where Cost(x, x′) is a cost function defined over pairs of points in R^(n). We then set WN_(p)(i, k,−1, d)=(i′, k′). Similarly, for all the points (i, k) in the Left list we search for a right neighbor point (i′, k′) in the Right list, with i′≠i, which minimizes Cost(WG_(p)(i, k), WG_(p)(i′, k′)) and set WN_(p)(i, k, 1,d)=(i′, k′). To guarantee that the neighborhood connection graph is symmetrical i.e. that WN _(p)(i, k, 1, d)=(i′, k′)

WN _(p)(i′, k′, −1, d)=(i, k) for each point (i, k) of the Left list, if WN_(p)(i, k, 1, d)=(i′, k′) and WN_(p)(i′, k′, −1, d)≠(i, k) we set WN_(p)(i, k, 1, d)≠nil which means that it has no right neighbor. Similarly, for each point (i, k) of the Right list, if WN_(p)(i, k, −1, d)=(i′, k′) and WN_(p)(i′, k′, 1, d)≠(i, k) we set WN_(p)(i, k, −1, d)≠nil.

A first example of cost function is derived from a norm in R^(n) for some r>0 Cost(WG _(p)(i, k), WG _(p)(i′, k′))=∥WG _(p)(i, k)−WG _(p)(i′, k′)∥r. A second example computes a distance after translating the point in the Left list by a prediction vector T_(i,k) in the direction d Cost(WG _(p)(i, k), WG _(p)(i′, k′))=∥WG _(p)(i, k)+T _(i,k) −WG _(p)(i′, k′)∥r where we may choose T_(i,k)=WG_(p)(i, k)−WG_(p)(i, k −1_(d)). Any other cost function may be used to compute the neighborhood connection graph WNp.

The warped subband filtering processor (803) performs a one-dimensional subband filtering of the warped coeflicients WC_(p) along the direction d, across the different grids, through the connections WN_(p) of the warping grids WG_(p) and outputs WC_(2p) and WC_(2p+1). The inverse aligned subband filtering processor (903) in FIG. 9 implements the inverse by reconstructing WC_(p) from WC_(2p) and WC_(2p+1), given WG_(p) and WN_(p). The detailed implementation of both processors is thus described together.

A state of the art one-dimensional uniform subband filtering is computed with a Finite Impulse Response low-pass filter H and an FIR high pass filter G, which admit a dual pair of FIR reconstruction filters H and G, as described in S. Mallat, “A wavelet tour of signal processing”, Academic Press, 1998. The subband decomposition performs a filtering with H and G of WC_(p)(i, k) along the direction d and subsamples the output on even index points within each grind.

Let S_(H) and S_(G) be the finite support of the filters H(l) and G(l) for l ε Z. If (i, k) satisfies ∀l ε S _(H) and ∀l ε S _(G) WG _(p)(i, 2_(d) k+l1_(d))≠nil   (6) then a state of the art uniform subband filtering along the direction d computes:

$\begin{matrix} {{{{WC}_{2p}\left( {i,k} \right)} = {\sum\limits_{l\; \in \; S_{H}}{{H(l)}{{WC}_{p\;}\left( {i,{{2_{d}k} + {l\; 1_{d}}}} \right)}}}}{and}} & (7) \\ {{{WC}_{{2p} + 1}\left( {i,k} \right)} = {\sum\limits_{l\; \in \; S_{G}}{G\;(l){{{WC}_{p}\left( {i,{{2_{d}k} + {l\; 1_{d}}}} \right)}.}}}} & (8) \end{matrix}$ For k=(k₁, . . . , k_(d), . . . , k_(n)) we write S _(H) +k_(d) and S _(G) +k_(d) the support of H and G translated by k_(d). The reconstruction is calculated with:

$\begin{matrix} \begin{matrix} {{{WC}_{p}\left( {i,k} \right)} = {{\sum\limits_{{2\; l}\; \in \;{S_{\overset{\sim}{H}} + k_{d}}}{{\overset{\sim}{H}\left( {{2\; l} - k_{d}} \right)}{{WC}_{2p}\left( {i,{k - {l\; 1_{d}}}} \right)}}} +}} \\ {\sum\limits_{{2\; l}\; \in \;{S_{\overset{\sim}{G}} + k_{d}}}{{\overset{\sim}{G}\left( {{2\; l} - k_{d}} \right)}{{{WC}_{{2p} + 1}\left( {i,{k - {l\; 1_{d}}}} \right)}.}}} \end{matrix} & \begin{matrix} (9) \\ (10) \end{matrix} \end{matrix}$ For signals of dimension n≧2, a phase aligned subband filtering computes WC_(2p) and WC_(p+1) from WC_(p) so that for all (i, k) which satisfy (6) the equality (7) and (8) are satisfied. The subband filtering processor (803) is then said to have a phase alignment coherent with the signal warping grids. To maintain this phase alignment property within each grid i, the subband filtering processor (803) adapts the filtering at the interface between warping grids. The inverse warped subband filtering processor (903) computes the inverse subband filtering (9) within each grid i and adapts the inverse filtering at the interface between warping grids. For signals of dimension n=1, the warped subband filtering processor (803) does not necessarily maintain the phase alignment property, which means that local shift may be introduced when calculating the warped coefficients.

In an exemplary implementation, the one-dimensional subband filtering along d is performed by the processors (803) and (903), by decomposing WG_(p) as a union of connected lines along the direction d, as follows. In both processors, we traverse W_(p) to build an array Begin(q)=(i, k) corresponding to grid points that do not have a left neighbor, which means that WN_(p)(i, k, −1, d)=nil. Each line indexed by q is initialized with L_(q)(0)=Begin(q) if k mod_(d)2=0 and L_(q)(1)=Begin(q) if k mod_(d)2=1. Then for any m≧0 we iteratively compute the right neighbor of L_(q)(m)=(i, k) and set L_(q)(m+1)=WN_(p)(i, k, 1, d) and stop when it is nil For each q fixed, we compute (7) and (8), with a direct subband filtering of L_(q).

We maintain the phase alignment property by imposing that even samples of L_(q) correspond to even samples of the grids WC_(p)(i, k) along the direction d: if (i, k)=L_(p)(m) then k mod_(d)2=m mod2. To enforce this property, points are inserted with the following procedure. Because of our initialization procedure, the first point L_(q)(0) or L_(q)(1) satisfies this alignment property. Then we traverse L_(q) and insert a new point with a special symbol. Insert when the phase alignment is not reacted For all m≧1, if (i, k)=L_(q)(m) and k mod_(d)2≠m mod 2 then for all 1≧m the entry L_(q)(l) is shifted to L_(q)(l+1) and we set L_(q)(m)=Insert, until the end of the line where L_(q)(m)=nil. In dimension n=1, not maintaining the phase alignment property for the processors (803) and (903) means not inserting new points along each line L_(q)(m), in which case we ray have (i, k)=L_(p)(m) and k mod_(d)2≠m mod 2.

To implement the processor (803) the warped coefficients values WC_(p)(i, k) along the line L_(q)(m) are stored in C_(q)(m). If L_(q)(m)≠nil and L_(q)(m)≠Insert then C_(q)(m)=WCp₍L_(q)(m)). If L_(q)(m)=(i, k) is a point sufficiently far from the grid boundary and satisfies (6) then the values Cq(m) are filtered with the same procedure as in (7) and (8). Otherwise, the filters are adapted to the interface between the connected grids to guarantee the perfect reconstruction property. In an exemplary implementation, the connecting filters are implemented by inserting new samples at grid connections where L_(q)(m)=Insert and using these inserted values in a filtering scheme using the same filters H and G. Each inserted value C_(q)(m) is expressed in terms of all other values C_(q)(l), including other inserted coefficients, with linear intpolation coefficients I_(m)(l) Let N(q) be the number of inserted values where L_(q)(m)=Insert along a line L_(q). We call S_(q) the set of indexes m such that L_(q)(m)≠nil and S_(i,q) the set of indexes m such that L_(q)(m)=Insert. The unknown value of al these inserted coefficients are calculated by solving the N(q) by N(q) linear system:

$\begin{matrix} {{\forall{m \in S_{i,q}}},{{e(m)} = {{\sum\limits_{l\; \in S_{q}}{{I_{m}(l)}{C_{q}(l)}}} = 0}}} & (11) \end{matrix}$ with I_(m)(m)=−1. If the linear system (11) does not have a unique solution, then we compute the vector of inserted coefficients (C_(q)(m))_(mεS) _(i,q) of minimum Euclidean norm which minimizes Σ_(mεS) _(i,q) |e(m)|², with a singular value decomposition. In a preferred embodiment, Σ_(lεS) _(q) I_(m)(l)=0 and the coefficients I_(m)(l) are convolution kernel coefficients I_(m)(l)=I(m−l), where border problems when defining I_(m) for extreme values of m are solved with a state of the art technique. If I(−l)=I(l), one may use a a symmetric extension of C_(q)(l) beyond its support. In an exemplary embodiment we choose I(l)=½ if I=1 and l=−1, and I(l)=0 if |l|>1. Since there is no consecutive inserted coefficients in L_(q)(m) in this case the solution of (11) is computed explicitly by

${\forall{m \in s_{i,q}}},{{C_{q}(m)} = {\frac{1}{2}{\left( {{C_{q}\left( {m - 1} \right)} + {C_{q}\left( {m + 1} \right)}} \right).}}}$

Warped subband coefficients are then calculated with a subband filtering of the line values:

$\begin{matrix} {{{D_{q}\left( {2m} \right)} = {\sum\limits_{I \in \; S_{H}}{{H(l)}{C_{q}\left( {{2m} + l} \right)}}}}{and}} & (12) \\ {{D_{q}\left( {{2m} + 1} \right)} = {\sum\limits_{I \in S_{G}}{{G(l)}{{C_{q}\left( {{2m} + l} \right)}.}}}} & (13) \end{matrix}$ Then values of WC_(2p) and WC_(2p+1) arrays are computed as follows: if L_(q)(2m)=(i, k)≠Insert then WC_(2p)(i, k div_(d)2)=D_(q)(2m) and if L_(q)(2m+1)=(i, k)≠Insert then WC_(2p+1)(i,k div_(d)2)=D_(q)(2m+1). Observe that the coefficients D_(q)(m) corresponding to inserted coefficients, i.e. such that L_(q)(m)=Insert, are discarded. If (i,k) satisfies (6) then (12) and (13) give the same result as (7) and (8). State of the art boundary techniques explained for example in S. Mallat, “A wavelet tour of signal processing”, Academic Press, 1998, such as symmetric procedures are used to solve boundary issues in the convolutions (12) and (13). In an exemplary implementation, these convolutions are computed with a state of the art lifting scheme as in I. Daubechies and W. Sweldens, “Factoring wavelet transfonns into lifting steps”, Jour. of Fourier Anal. Appl., Vol. 4, No. 3, pp. 245-267, 1998. In an exemplary implementation, the filters H and G are chosen to be the 7/9 Cohen, Daubechies, Feauveau filters specified in the above reference.

For a fixed q, let M(q) be the size along m of the vectors C_(q)(m) and D_(q)(m) and N(q) be the number of inserted values corresponding to m ε S_(i,q). We denote by O_(q) the linear operator from R^(M(q)) to R^(M(q)) which associates to any vector C_(q)(m) the vector D_(q)(m) with the filtering and subsampling formulas (12) and (13). The values of the line q in the direction d of the input WC_(p) and of the outputs WC_(2p) and WC_(2p+1) are stored in the vectors C _(q)(m) and D _(q)(m) of size M(q)−N(q), that equal respectively to C_(q)(m) and D_(q)(m) for all m ε S^(q)−S_(i,q). Inserting coefficients according to (11), computing the coefficients D_(q)(m) according to formulas (12) and (13) and then discarding coefficients D_(q)(m) corresponding to inserted coefficients for indexes m ε S_(i,q) defines an operator Ō_(q) from R^(M(q)−N(q)) to R^(M(q)−N(q)) that maps the vector (C_(q)(m))_(mεS) _(q) _(−S) _(i, q) to (D_(q)(m))_(mεS) _(q) _(−S) _(i,q) . This linear operator Ō_(q) performs an adaptive filtering, with modified filter coefficients over the points L_(q)(m)=(i, k) which do not satisfy (6). Any other state of the art implementation of Ō_(q), including a direct lifting scheme with no coefficient insertion, may be used to compute D _(q)(m) and hence each line q of WC_(2p) and WC_(2p+1).

The inverse processor (903), recovers the warped coefficiens values WC_(p) along each line L_(q)(m) from the values of WC_(2p) and WC_(2p+1) along the corresponding lines. The vectors Ī_(m) are defined by Ī_(m)=O_(q) ^(−T)I_(m). Their expression is then for indexes m away from the boundaries:

$\begin{matrix} {{{{\overset{\sim}{I}}_{m}\left( {2r} \right)} = {\sum\limits_{I \in S_{\overset{\sim}{H}}}{{\overset{\sim}{H}(l)}{I_{m}\left( {{2r} - l} \right)}}}}{and}} & (14) \\ {{{\overset{\sim}{I}}_{m}\left( {{2r} + 1} \right)} = {\sum\limits_{I\; \in \; S_{\overset{\sim}{G}}}{{\overset{\sim}{G}(l)}{{I_{m}\left( {{2r} - l} \right)}.}}}} & (15) \end{matrix}$ When the coefficients I_(m)(l) are convolution kernel coefficients, the coefficients of Ī_(m)(l) have a simple structure: Ī_(m)(l)=Ī₀(l−m) for even m and Ī_(m)(l)=Ī₁(l−m) for odd m, where Ī₀ and Ī₁ are two filters. This is not true near the boundaries, where the computation of Ī_(m)=O_(q) ^(−T)I_(m) is obtained by using appropriate boundary techniques when evaluating the sum (14) and (15), which are apparent to those skilled in the art

Each array D_(q) is defined for indexes m ε S_(q)−S_(i,q) as follows: if L_(q)(2m)=(i,k)≠Insert then D_(q)(2m)=WC_(2p)(i, k div_(d)2) and if L_(q)(2m+1)=(i,k)≠Insert then D_(q)(2m+1)=WC_(2p+1)(i, k div_(d)2). To compute D_(q)(m) for all m ε S_(i,q) we solve the system of N(q) linear equations with N(q) unknowns:

$\begin{matrix} {{\forall{m \in \; S_{i,q}}},{{\overset{\sim}{e}(m)} = {{\sum\limits_{l\; \in \; S_{q}}{{{\overset{\sim}{I}}_{m}(l)}{D_{q}(l)}}} = 0}}} & (16) \end{matrix}$ If the linear system (16) does not have a unique solution, then we compute the vector of inserted coefficients (D_(q)(m))_(mεS) _(i,q) of minimum Euclidean for which minimizes Σ_(mεS) _(i,q) |ē(m)|², with a singular value decomposition

An inverse subband filtering is then applied on the line values D_(q)(m). For all l ε S_(q)−S_(i,q)

$\begin{matrix} \begin{matrix} {{C_{q\;}(I)} = {{\sum\limits_{{2m} \in {l - S_{\hat{H}}}}{{\overset{\sim}{H}\left( {l - {2m}} \right)}D_{q}\left( {2m} \right)}} +}} \\ {\sum\limits_{{2m} \in \;{l - S_{\hat{G}}}}{{\overset{\sim}{G}\left( {l - {2m}} \right)}{D_{q}\left( {{2m} + 1} \right)}}} \end{matrix} & (17) \end{matrix}$ and WC_(p)(L_(q)(m))=C_(q)(m). State of the art boundary techniques, as described in S. Mallet “A wavelet tour of signal processing”, Academic Press, 1998, such as symnmetric procedures are used to solve boundary issues in the two convolutions (17). In an exemplary implementation, these convolutions are computed with a state of the art lifting scheme as in I. Daubechies and W. Sweldens, “Factoring wavelet transforms into lifting steps”, Jour. of Fourier Anal. Appl., Vol. 4, No. 3, pp. 245-267, 1998. For each q, the insertion system (16) together with (17) computes the inverse Ō_(q) ⁻¹ of the linear operator Ō_(q) previously defined. Any other implementation of Ō_(q) ⁻¹ may be used, including a lifting scheme. In dimension n=1 if the phase alignment property is not maintained then there is no inserted coefficients and the system (16) is therefore not used.

In yet another implementation of the warped subband filtering processor (803) in direction d and its inverse (903), the subband filtering takes into account the fact that points in the sampling grids WG_(p)(i, k) are irregularly spaced, using subband filterng on irregular point sets. In this embodiment, the position (abscissa) of each point of a line L_(q)(m) is stored in X_(q)(m). For all (q,m) with L_(q)(m)≠Insert and L_(q)(m)≠nil, we set X_(q)(m)=WG_(p)(L_(q)(m)).1_(d), or any curvilinear abscissa defined along the polygonal line joining the points (WG_(p)(L_(q)(m)))_(mεS) _(q) _(−S) _(i,q) . If m ε S_(i,q) then

${X_{q}(m)} = {\frac{1}{2}\left( {{X_{q}\left( {m - 1} \right)} + {X_{q}\left( {m + 1} \right)}} \right)}$ and if L_(q)(m)=nil then X_(q)(m)=nil.

To implement the warped subband filtering processor (803), the value of C_(q)(m) at the N(q) inserted positions where L_(q)(m)=Insert are calculated by solving an N(q) by N(q) linear system:

$\begin{matrix} {{\forall{m \in S_{i,q}}},{{\sum\limits_{l \in S_{q}}{{I_{m}(l)}{C_{q}(l)}}} = 0}} & (18) \end{matrix}$ with I_(m)(m)=−1. If this system does not have a single solution, the inserted values are calculated with a singular value decomposition as previously explained. For l≠m, the values of I_(m),(l) may depend upon m and be obtained by any state of the art interpolation kernel for irregular spaced samples located at positions X_(q)(m), such as a Lagrange interpolation.

The low-pass and highbpass convolutions (12) and (13) are then replaced by a state of the art subband filtering procedure for irregular point sets. In an exemplary implementation, such a subband filtering, also called subdivision scheme, or one step wavelet transform, can be calculated with a lifting scheme, with any state of the art procedure such as the 1D subdivision in I. Daubechies, I. Guskov, P. Schröder and W. Sweldens, “Wavelets on irregular point sets”, Phil. Trmas R. Soc. Lond. A., Vol. 357, No. 1760, pp. 2397-2413, 1999. For a fixed position X_(q)(m) of the sample points, the irregular subband filtering operator that associates D_(q)(m) to C_(q)(m) is a linear operator that we also write O_(q), ie. D_(q)=O_(q)C_(q).

In an exemplary implementation of the inverse warped subband filtering processor (903), the value of D_(q)(m) for m ε S_(i,q), are computed with the inverse kernel Ī_(m)=O_(q) ^(−T)I_(m), by solving the system

$\begin{matrix} {{\forall{m \in S_{i,q}}},{{\sum\limits_{l \in S_{q}}{{{\overset{\sim}{I}}_{m}(l)}{D_{q}(l)}}} = 0.}} & (19) \end{matrix}$

The solution is calculated with a singular value decomposition if the system does not have a unique solution. The inverse subband filtering operator O_(q) ⁻¹ for irregular point sets is then applied on the line values D_(q)(m) to recover C_(q)(m). For al m ε S_(q)−S_(i,q) we set WC_(p)(L_(q)(m))=C_(q)(m). The insertion system (19) together with O_(q) ⁻¹ computes the inverse Ō_(q) ⁻¹ of the linear operator Ō_(q) previously defined. Any other implementation of Ō_(q) ⁻¹ may be used, including a lifting scheme.

The inverse warped wavelet packet processor (304) in FIG. 3 implements the inverse of the warped wavelet packet processor (204) in FIG. 2 it takes in input a warped wavelet packet transform including the warped coefficients at the leaves of a wavelet packet binary tree together with their warping grids, and it computes a warped signal. FIG. 10 shows an exemplary configuration of an inverse warped wavelet packet processor implemented with Inverse Warped Subband Processors (IWSP) that are distributed along the nodes of the same binary tree as the one illustrated in FIG. 5 and used by the warped wavelet packet processor (204). At each node p of this tree, the warped coefficients WC_(p) and its warping grid W_(G) p are reconstructed with an inverse warped subband processor in a particular direction d, from the children warped coefficients WC_(2p) and WC_(2p+1) together with their warping grids WG_(2p) and WG₂₊₁. The inverse warped wavelet packet processor outputs a warped signal including the warping grid WG₁ and the warped coefficients WC₁.

FIG. 9 shows, in block diagram form, an exemplary configuration of an Inverse Warped Subband Processor (IWSP) along direction d. It implements the inverse of the warped subband processor illustrated in FIG. 8. The processor (901) performs grid union in direction d by taking in input the warping grids WG_(2p) and WG_(2p+1) and computing WG _(p)(i, 2_(d) k)=WG _(2p)(i, k) and WG _(p)(i, 2_(d) k+1_(d))=WG _(2p+1)(i,k), and RegWG_(p)(i)=2_(d) RegWG_(2p)(i). In (902) the neighborhood connection processor in direction d is identical to the neighborhood connection processor (802) in FIG. 8. It computes a warped neighborhood map WN_(p) which is an input together with WG_(p) and the warped coefficients WC_(2p) and WC_(2p1) in the inverse warped subband filtering processor (903) along direction d. This processor outputs the warped coefficients WC_(p). Given the warped neighborhood map WN_(p) and the warping grid WC_(p), the processor (903) implements the inverse of the warped subband filtering processor in (803). Its detailed implementation was described together with the processor (803). Warping Grid Subsampling

The wavelet packet warping grid subsampling module (302) in FIG. 3 takes in input a warping grid WG and computes wavelet packet warping grids, with a procedure illustrated by the block diagram in FIG. 11. For convenience, the input warping grid is relabeled WG₁. This warping grid is then subsampled in different directions along the same binary tree as the one used to compute the warped wavelet packet processor (204). At each node p, the grid subsampling in direction d_(p) is identical to the grid subsampling module in direction d that appears in (801) and was previously defined The wavelet packet warping grids is the family of all warping grids WG_(p) at all leaves p ε P of the binary tree. In FIG. 11, it corresponds to WG₄, WG₇, WG₁₀, WG₁₁, WG₁₂ and WG₁₃, i.e. P={4,7,10,11,12,13}.

Bandeletisation and Inverse Bandeletisation

The bandeletisation processor (205) takes in input a warped wavelet packet transform composed of warped coefficients (W_(p))_(pεP) and corresponding warping grids (WG_(p))_(pεP) and outputs bandelet coefficients, by performing a sequence of 1-dimensional decorrelations along selected directions on the warped wavelet packet coefficients to reduce identified correlations in the signal and obtain a new, sparser representation of the signal. FIG. 12 illustrates an exemplary embodiment of a bandeletisation processor. For each input warping grid, the direction selection processor (1201) compute a decorrelation descriptor DirWG_(p)(i) depicting what kind of decorrelation is to be performed on a region i of warping grids WG_(p). The input warping grids WG_(p) and warped coefficient arrays WC_(p) are split in module (1202) into subgrds WG_(pσ) and subways WC_(pσ)wherein the decorrelation descriptor is uniform and equal to some DirWG_(pσ). The subarrays WC_(pσ) are then transformed in the bandeletisation along DirWG_(pσ) (1203) into a set of bandelet coefficients.

The inverse bandeletisation processor (303) takes in input wavelet packet warping grids (WG_(p))_(pεP) and bandelet coefficients and outputs a warped wavelet packet transform composed of computed warped wavelet packet coefficients (WC_(p))_(pεP), and of input wavelet packet warping grids (WG_(p))_(pεP). FIG. 13 illustrates an exemplary embodiment of an inverse bandeletisation processor. Module (1301) computes decorrelation descriptors exactly as in module (1201). Module (1302) splits grids WG_(p) into grids WG_(pσ) exactly as in module (1202). The inverse bandeletisation along DirWG_(pσ) (1303) computes from input bandelet coefficients warped coefficient arrays WC_(pσ), which are then merged back into arrays WC_(p) in the warped grid merging processor (1304).

In an exemplary embodiment the regularity descriptor of a grid RegWG_(p)(i) is a vector of n reals (b₁, . . . , b_(n)) that are dyadic or zero, and indicates what kind of warped coefficient regularity is reacted for each grid WG_(p) and each region i. For each direction d, b_(d)=0 means that no regularity has been detected along direction d, 0<b_(d)≧1 means that the warped coefficient array WG_(p) has slow variations along direction d, and b_(d)>1 means that the signal has close to periodic variations along direction d, of period b_(d).

Depending on this regularity descriptor, a decorrelation descriptor DirWG_(p)(i) is computed in module (1201) for each region i and each warping grid WG_(p). The decorrelation descriptor is a vector of n integers, (β₁, . . . , β_(n)) indicating for each direction d if a decorrelation operation has to be performed along direction d (when β_(d)>0), and then what kind of decorrelation has to be performed along that direction d in module (1203). Hence when β_(d)=0, no decorrelation along direction d is to be perform When β_(d)=1, a decorrelation operator is applied along direction d, like for example a wavelet or wavelet packet transform, or a discrete cosine transform. When β_(d)>1, a decorrelation operator performing a periodic decorrelation of period β_(d) is applied. In an exemplary embodiment a decorrelation operator performing a periodic decorrelation of period β_(d) for a 1-dimensional array of coefficients (L(m))_(mεM) is implemented with a one-dimensional wavelet or wavelet packet transform, or a discrete cosine transform applied to subarrays of L of stride β_(d), i.e. on the subarays (L(β_(d)m+r))β_(d)m+rεM, for r=0, . . . , β_(d) −1.

In an exemplary embodiment, the decorrelation descriptor DirWG_(p)(i) is computed from RegWG_(p)(i)=(b₁, . . . , b_(n)) by setting DirWG_(p)(i)=(β₁, . . . , β_(n)) where β_(d)0 if b_(d)=0, β_(d)=1 if 0<b_(d)≦1, and β_(d)=b_(d) otherwise. In a degenerate case where RegWG_(p)(i)=(0, . . . , 0). the decorrelation descriptor is equal to (0, . . . , 0), in which case the bandeletisation performs no decorrelation and outputs coefficients that are the inputwrped wavelet paketcoefficienis.

In yet another exemplary embodiment wherein the input warped wavelet packet coefficients are warped wavelet coefficients, the decorrelation descriptor DirWG_(p)(i) is computed in the same way as above, except when 0<b_(d)<1, in which case β_(d)=1 if the warped wavelet coefficients of WC_(p) are low-pass along the direction d and β_(d)=0 otherwise. Warped wavelet coefficients WC_(p) are said to be low-pass along the direction d if in the chain of warped subband processings to compute WC_(p) from WC₁, WC_(p) is the low-pass output of the last warped subband processing in direction d, or if it has been computed from this low-pass output with further warped subband proessings in other directions but not in the diection d.

In yet. another exemplary embodiment, the decorrelation descriptor DirWG_(p)(i) is computed according to one of the two above embodiments, except when all 0<b_(d)<1 for all d, in which case we set DfrWG_(p)(i)=(0, . . . , 0) instead his corresponds to the case where no further decorrelation is necessary because the warped signal is informly regular in all directions and hence the the warped wavelet packet coefficients array is already sparse. The same descriptors are needed in the inverse bandeletisadon processor, so module (1301) is identical to (1201).

In an exemplary embodiment, the bandeletisation processor applies decorrelation operators separately on each region i. In a preferred embodiment, the bandeletisation processor operates across regions whenever this is possible, i.e. whenever two regions are proceseed according to the same decorrelation descriptor DirWG_(p)(i). An exemplary method to apply different decorrelation operators for warping grids corresponding to different regions i consists in first splitting the input warping grids WG_(p) and warped coefficient arrays WC_(p) into grids (WG_(pσ))_(σεΣ) and subarrays (WC_(pσ))_(σεΣ), over which the decorrelation descriptor DirWG_(p)(i) is the same, and then computing neighborhood connection graphs WN_(pσ) for each resulting grid WG_(pσ). In module (1202), WG_(pσ) and WC_(pσ) are then defined by WG_(pσ)(i, k)=WG_(p)(i, k) if DirWG_(p)(i)=σ and WG_(pσ)(i,k)=nil otherwise, and similarly WC_(pσ)(i,k)=WC_(pσ)(i,k) if DirWG_(p)(i)=σ and WC_(pσ)(i, k)=nil otherwise, where the σ index is a decorrelation descriptor. The case where the decorrelation operators act separately on each grid is implemented by splitting the warping grids WC_(p) and warped coefficient may WC_(p) according to the region index. We set WG_(pσ)(i, k)=WG_(p)(i, k) if σ=i and WG_(pσ)(i, k)=nil otherwise, and similarly for WC_(pσ), where σ is now a region index. In all cases, the decorrelation descriptor DirWG_(p)(i) is the same for all region indexes i of the warping grids WG_(pσ), (i.e. all i such that there exists a k with WG_(pσ)(i,k)≠nil). We thus write this decorrelation descriptor DirWG_(pσ). The decorrelation dimension DimWG_(p) of the wavelet packet warping grids (WG_(p))_(pεP) is then defined as the maximum number of nonzero entries in a vector DirWG_(pσ) for all possible p and σ. It is the maximum number of directions along which a bandelet decorrelation will be performed within a single grid.

In an inverse bandeletisation, the warping grids WG_(pσ) are computed from the warping grids WG_(p) in module (1302) in the same way as in module (1202). Module (1302) differs however from module (1202), in that it does not compute and output the warping coefficients WC_(pσ).

In the degenerated case where all vectors DirWG_(pσ) are zero vectors (0, . . . , 0), then the decorrelation dimension DimWG_(p) is equal to 0 and the bandeletisation outputs bandelet coefficients equal to the input warped wavelet packet coefficients.

If the decorrelation dimension DimWG_(p) is equal to 1 then the number of directions along which a decorrelation operator has to be applied is at most equal to 1, i.e. each vector DirWG_(pσ) has at most one nonzero entry. This happens most often for 1-dimensional or 2dimensional input signals. In this case, the decorrelation operator operates along lines of the warped wavelet packet coefficients, and in particular inside each grid along a single direction. This particular implementation of a bandeletisation is called a one-dimensional bandeletiaion. For each set of warping grids WG_(pσ), the vector DirWG_(pσ) either contains a single coefficient β_(d)>0, in which case the conducting d is denoted d_(pσ) and the corresponding value β_(d) is denoted β_(pσ), or DirWG_(pσ) only contains zero entries, in which case we set β_(pσ)=d_(pσ)=nil. The bandeletisation along DirWG_(pσ) in module (1203) then consists in applying when d_(pσ)≠nil a decorrelation operator along separate lines of each warped coefficient array WC_(pσ) along the single direction d_(pσ). In an exemplary embodiment the module (1203) computes for each set of warping grids WG_(pσ) a neighborhood connection graph WN_(pσ)(i, k,±1, d_(pσ)) as done for grids WG_(p) in module (802). Then, each set of warping grids WG_(pσ) is decomposed into a union of connected lines along direction d_(pσ). To do this, we traverse WN_(pσ) to build an array of line beginning points Begin_(pσ)(q)=(i, k) that are points of WG_(pσ) that have no left neighbor: WN_(pσ)(i, k, −1, d_(pσ))=nil. Each line indexed by q of WG_(pσ) commences with L_(pσq)(0)=Begin_(pσ)(q). The line is iteratively augmented with the right neighbor of the preceding point: if (i, k)=L_(pσq)(m), L_(pσq)(m+1)=WN_(pσ)(i, k, 1, d_(pσ)), until that point is nil.

Then, each line of coefficients C_(pσq)(m)=WC_(pσ)(L_(pσq)(m)) for p and σ such that β_(pσ)=1 is transformed into a new array D_(pσq) with a one dimensional inverible decorrelation operrtor. In an exemplary embodiment, this invertible decorreiation operator may be a wavelet transform a wavelet packet transform, a discrete cosine or sine transform computed with a fast algorithm as described in S. Mallat, “A wavelet tour of signal processing”, Academic Press, 1998. Each line of coefficients for which β_(pσ)>1 is transformed with a one-dimensional decorrelation operator performing a periodic decorrelation of period β_(pσ). In an exemplary embodiment of a periodic decorrelation operator, for each r=0, . . . , β_(pσ)−1, the subanray (C_(pσq)(β_(pσ)m+r))m is transformed by the one-dimensional invertible decorrelation operator, and the result is stored into (D_(pσq)(β_(pσ)m+r))_(m). The transformed coefficients of D_(pσq) are then stored back into arrays WC_(pσ0) by setting WC_(pσ0)(L_(pσq)(m))=D_(pσq)(m). When d_(pσ)=nil, the array WC_(pσ0) is equal to WC_(pσ). The bandelet transform of the warped coefficient arrays (WC_(p))_(pεP) is then composed of the coefficient arrays WC_(pσ0) for all p and σ.

If the decorrelation dimension DimWG_(p) is equal to 1 then the inverse bandeletisation is called an inverse one dimensional bandeletisation and it implements the inverse transform of the one-dimensional bandeletisation. In this case, the inverse bandeletisation along DirWG_(pσ) of (1303) is implemented with the same procedure as (1203), but by replacing the one-dimensional decorrelation operators used in the bandeletisation along DirWG_(pσ) by their inverses, called one-dimensional inverse decorrelation operators. In the exemplary embodiment, where the decorrelation operators are either wavelet transforms, wavelet packet transforms, discrete cosine or sine transforms, their inverse is computed with fast algorithms described in S. Mallat, “A wavelet tour of signal processing”, Academic Press, 1998. The inverses of one-dimensional decorrelation operators performing a periodic decorrelation are called periodic inverse decorrelation operators.

We now describe the implementation of a bandeletisation when the decorrelation dimension DimWG_(p) is strictly larger than 1. In this case there is at least one region where the bandeletisation applies one-dimensional decorrelation operators along different directions inside the same region. Inside any such region each decorrelation operator must then be phase-aligned. The bandeletisation is then called a phase-aligned bandeletisation. In a preferred embodiment, the bandeletisation along DirWG_(pσ) is computed with phase aligned warped wavelet packet tansforms, which are applied separately to each pair WG_(pσ) and WC_(pσ). Each one-dimensional decorrelation operator is then implemented with a phase-aligned warped subband processing. For each p and σ, we write DirWG_(pσ) as (β_(pσ1), . . . , β_(pσn)). To implement periodic decorrelations with phase aligned warped wavelet packet transforms, in an exemplary embodiment the warping grids WG_(pσ) and warped coefficients WC_(pσ) are subsampled along each direction d for which, β_(pσd)>1. To include the non-periodic decorrelation in the same framework, the grids are subsampled by a factor of β_(pσd) if β_(pσd)>1 and not subsampled, or equivalently subsampled by a factor 1, if β_(pσd)=0 or 1. We thus define β _(pσd)=1 if, β_(pσd)=0 and β _(pσd)=β_(pσd) otherwise. For each p and σ, we define R_(pσ) as the set of integer vectors r=(r₁, . . . , r_(n)) such that 0≦r_(d)< β _(pσd) for d=1, . . . , n. The subsampled warping grids and warped coefficient arrays WG_(pσr) and WC_(pσr) are then defined for each r ε R_(pσ) as WG _(pσr)(i, k ₁ , . . . , k _(n))=WG _(pσ)(i, k ₁ β ₁ +r ₁ , . . . , k _(n) β _(n) +r _(n)) WC _(pσr)(i, k ₁ , . . . , k _(n))=WC _(pσ)(i, k ₁ β ₁ +r ₁ , . . . , k _(n) β _(n) +r _(n)) This subsampling for each p, σ yields a family of warping grids (WG_(pσr))_(pεP, σεΣ, rεR) _(p σ) and warped coefficient arrays (WC_(pσr))_(pεP,σεΣ,rεR) _(pσ) . Each pair WG_(pσr), WC_(pσr) then undergoes a phase warped wavelet packet transform exemplified in FIG. 5, with a binary tree T_(p,σ) of one-dimensional warped subband processings to complete (WG_(pσrq))_(qεQ) _(pσ) and (WC_(pσrq))_(qεQ) _(pσ) . Each binary tree T_(pσ) includes one-dimensional decorrelation operators that are phase aligned ward subband processings performed only in directions d for which β_(pσd)>0. In an exemplary embodiment, the binary tree T_(pσ) is a warped wavelet transform binary tree over the subset of directions d for which β_(pσd)>0. When a coefficient array WC_(pσr) is subsampled along direction d with β _(pσd)>0, the phase aligned warped subband processing along direction d is said to perform a periodic decorrelation. The bandelet coefficients, which are the output of the bandeletisation illustrated in FIG. 12 are the coefficients (WC_(pσrq))_(pεP,σεΣ,rεR) _(pσ) _(,σεQ) _(pσ) . These are the input of the inverse bandeletisaion illustrated in FIG. 13.

The inverse phase-aligned bandeletisation implements the inverse of the operator implemented by the phase-aligned bandeletisation processor. In this case, the inverse bandeletisation along DirWG_(pσ) (1303) computes from the warping grids WG_(pσ) all the subsampled warping grids WG_(pσr) with the same procedure as in the module (1203). Then for each p, σ, r, the warping grids (WG_(pσrq))_(qΣQ) _(pσ) are computed from WG_(pσr) with a tree of grid subsamplings corresponding to T_(pσ), as illustrated in FIG. 11. For each p, σ, r, the warping grids (WG_(pσrq))_(qεQ) _(pσ) and warped coefficient arrays (WC_(pσrq))_(qσQ) _(pσ) are transformed by a phase aligned inverse warped wavelet packet processor implemented with the biary tree T_(pσ) of phase aligned inverse warped subband processings, to compute a warped coefficient array WC_(pσr). When β _(pσd)>0, the phase aligned inverse warped subband processings are said to perform a periodic inverse decorrelation. Then, for each p and σ, the warped coefficient arrays (WC_(pσr))_(rεR) _(pσ) are merged back into WC_(pσ) by setting WC _(pσ)(i, k ₁ β ₁ +r ₁ , . . . , k _(n) β _(n) +r _(n))=WC _(pσr)(i, k ₁ , . . . k _(n)) for each r in R_(pσ) and k such that WG_(pσ)(i, k₁ β ₁+r₁, . . . , k_(n) β _(n)+r_(n))≠nil.

For both the inverse one-dimensional bandeletisation processor or the inverse phase-aligned bandeletisation processor, for each p ε P the warped coefficient arrays (WC_(pσ))_(σεΣ) are merged back into WC_(p) in the warping grid merging processor (1304) by setting WC_(p)(i, k)=WC_(pσ)(i, k) if σ is such that WG_(pσ)(i, k)≠nil.

It is apparent to those skilled in the art that is also possible to combine one-dimensional bandeletisation and phase-aligned bandeletisation. Denoting Dim WG_(pσ) the decorelation dimension of WG_(pσ), which is the number of nonzero entries in DirWG_(pσ), the combined bandeletisation consists in applying a one-dimensional bandeletisation on all grids WG_(pσ) such that DimWG_(pσ)=1, and applying a phase-aligned bandeletisation on all grids WG_(pσ) such that DimWG_(pσ)>1. The same rule is then used for the inverse combined bandeletisation.

Restoration System

A restoration system is implemented by using a restoration r in the processor (102) of FIG. 1. A restoration processor computes a processed warping geometry and processed bandelet coefficients, and an inverse warped wavelet packet bandelet processor takes in input the processed warping geometry and processed bandelet coefficients to reconstruct a restored signal.

In an exemplary embodiment, a restoration processor removes additive noises, by applying diagonal operators to bandelet coefficients. A diagonal operator applied to a bandelet coefficients stored in WC_(q)(i, k) computes θ_(q)(WC_(q)(i, k)) where θ_(q)(x) is a linear or non-linear function. The function θ_(q) can be chosen to be a thresholding fimction which sets to zero the bandelet coefficients whose amplitude are smaller than a threshold T_(q) whose value adapted to the noise properties. Examples of state-of-the-art linear or thresholding estimators are described in S. Mallat, A Wavelet Tour of Signal Processing, 2nd edition. Academic Press, San Diego, 1999. A regularization operator can also be applied to the warping geometry calculated from the input noisy signal, in order to suppress the effect of the noise on the signal geometry.

A restoration processor can also implement a signal deconvolution system, for example to deblur 2-dimensional images. In an exemplary embodiment, a diagonal operator is applied to bandelet coefficients here the diagonal operator is designed to approximate the deconvolution filter by multiplying each bandelet coefficient with an appropriate constant. A restoration processor can also implement any state of the operators on the bandelet coefficients and the warping geometry to restore an output signal for restoration systems, including inverse scattering systems, tomographic reconsruction systems, interpolation systems, or superresolution systems which reconstruct higher resolution signals.

Compression System

FIG. 14 illustrates an exemplary implementation of a signal compression system using the warped wavelet packet bandelet processor illustrated in FIG. 2. The input signal is transformed in (1401) into a warping geometry and a set of bandelet coefficients. The warping geometry is computed by a geometrical segmentation module (201) in FIG. 2, which includes a quantizer to compute the deformation parameters that specify the geometrical deformations in each signal region. The quantizer (1402) outputs quantized bandelet coefficients that are encoded together with the warping geometry by a binary coder (1403) to produce a bitstream. The quantizer (1402) may be any state of the art scalar or vector quantieer, adapted to the type of input signal and the configuration of the warped wavelet packet bandelet processor. In an exemplary embodiment, the quantizer is a uniform quantieer of a constant bin size T and with a zero bin twice larger than the other bins, and equal to [−T,T]. The biny coder (1403) may be any state of the art binary coder, including entropy coders such as an arithmetic coder, or a Huffman coder, or any entrppy coder, as in T. M. Cover and J. A. Thomas, Elements of Information Theory, Wiley Series in Telecommunications, John Wiley & Sons, 1991. The entropy coding may also use multiple contexts to further reduce the code size.

An exemplary embodiment of such a compression system when n=1 is a compression system for electro-cardiograms (ECG), where the periodic decorrelation in the bandeletisation is particularly pertinent for decorrelating the periodic structure of the ECG. Yet another exemplary embodiment of such a compression system when n=2 is an image compression system computed with a warped wavelet transform, with a warping geometry that defines regions that are unions of retangles. For particular class of images such as human faces, the warping geometry and its coding takes advantage of prior information known on this class of images. In yet another exemplary embodiment for special classes of images such as fingerprint images, the warped wavelet packet transform is adapted to the property of this class of images. The periodic decorrelation in the bandeletisation is pertinent for decorrelating the periodic ridge strugtles existing in fingerprint images.

Yet another embodiment of such a compression system when n=3 is a video compression system using a warping geometry that includes a directional geometric deformation obtained by applying time displacements to 2-dimensional geometrical deformations using estimated motion vectors. Yet another exemplary embodiment when n=3 of such a compression system is a compressor for seismic 3-dimensional volumetric data or for 3-dimensional medical data. Yet another embodiment of such a compression system when n=4 is a compressor for light field volumes of data, such as the data obtained by composing digital pictures of an object taken with varying tilt and pan angles.

FIG. 15 illustrates an exemplary implementation of a signal decompression system using the inverse warped wavelet packet and bandelet transform illustrated in FIG. 3. The processor (1501) decodes the input bitstre and outputs the warping geometry and the quantized bandelet coefficients, from which the inverse warped wavelet packet bandelet processor (1502), illustrated in FIG. 3, computes an output reconstructed signal.

Feature Extraction System

The present invention includes a system that computes a signal feature vector from the warping geometry and the bandelet coefficients, for pattern recognition applications including content based signal indexing and retrieval from a database, signal matching, or for detection and classification. The input signal is transformed into a warping geometry and bandelet coefficients by the warped wavelet packet bandelet processor illustrated in FIG. 2.

In a first configuration of the system illustrated in FIG. 16, the warping geometry and the bandelet coefficients of an input signal are computed with a warped wavelet packet bandelet processor (1601) and a feature vector is calculated from the warping geometry and the bandelet coefficients by using a state of the art feature extraction procedure (1602). In an exemplary embodiment, the feature vector is used for a content-based indexing system. The feature extraction can be computed with histogram techniques applied to transformed bandelet coefficients as in M. K. Mandal and T. Aboulnasr, “Fast wavelet histogram techniques for image indexing”, Computer Vision and Image Understanding, vol. 75, no. 1/2, pp. 99-110. Bandelet coefficients may be equal to warped wavelet coefficients for a degenerated bandeletisation where the decorrelation dimension is 0. The feature parameters obtained from the warping geometry describe geometrical signal properties whereas the parameters obtained from bandelet coefficients describe the evolution of the signal values along this geometry. For a content based signal retrieval from a data basis of signals, the signal feature vector is compared with the feature vectors of the signals stored in a data basis, using an appropriate distance measure. In yet another exemplary embodiment, feature vectors are used for signal matching and recognition by comparing the respective feature vectors of two signals. The feature extraction is performed with any state of the art procedure adapted to matching applications for a particular class of signals, for example fingerprint images or buman face images. In yet another exemplary embodiment, feature vectors are used to classify signals. For an electro-cardiogram, a feature vector can be used by a state of the art classifier to make a diagnosis on the electro-diogram signal and detect anomalies.

To reduce bandwidth and storage requirements, signals are represented in a compressed form. In a second exemplary configuration of the system illustrated in FIG. 17, the signal feature vector is computed (1701) from the compressed bitstream produced by the binary coder (1403) of the warped wavelet packet bandelet compression system illustrated in FIG. 14. Any state of the art technique may be used to compute the feature extraction. For content-based indexing and retrieval from a database, in an exemplary embodiment, the parameters of the feature extraction are obtained with state of the art histogram procedures as in E. Feig and C.-S. Li, “Computing image histogram from compressed data,” Proceedings of SPIE 2898, 1996, pp. 118-124. For matching or classification of signals such as fingerprint images, images of human faces or electro-cardiograms or any other type of signals, any state of the art feature extraction system can be used to compute the feature vector

While a detailed description of presently exemplary embodiments of the invention has been given above, various alternatives, modifications, and equivalents will be apparent to those skilled in the art. For example, while different components of the Warped wavelet packet processor and inverse warped wavelet packet processor of the present invention are described herein as performing certain specific functions, one skilled in the art will appreciate that other components or circuits in the service module may perform some or all of such functions without varying from the spirit of the invention. Therefore, the above description should not be taken as limiting the scope of the invention which is defined by the appended claims. 

1. A method of processing n-dimensional digital signals, n being an integer at least equal to 1, comprising the steps of: (a) receiving an n-dimensional digital input signal; (b) computing an n-dimensional warped signal from said n-dimensional digital input signal, the n-dimensional warped signal including n-dimensional warped coefficients and n-dimensional signal warping grids; and (c) computing warped wavelet packet coefficients and wavelet packet warping grids by applying an n-dimensional warped wavelet packet transform to said warped signal, with a binary tree where each node performs a one-dimensional warped subband processing along a respective dimension d, with 1≦d≦n; wherein said signal warping grids are computed from a warping geometry defined by region parameters specifying a partition of a signal support into a plurality of regions and deformation parameters specifying geometrical deformation functions respectively associated with said regions, whereby the geometrical deformation function associated with one of the regions provides positions of sampling points within said one of the regions; wherein n=3, said n-dimensional digital input signal represents a video image sequence, and wherein the step of computing the n-dimensional warped signal comprises estimating motion vectors within said video image sequence, determining at least one of said n-dimensional geometrical deformation functions by applying a time displacement to a 2-dimensional geometrical deformation using said estimated motion vectors, and computing said n-dimensional warped signal from said warping geometry and said n-dimensional digital input signal.
 2. A signal processing method according to claim 1, further comprising the step of applying a bandeletisation to said warped wavelet packet coefficients and wavelet packet warping grids, wherein said bandeletisation comprises computing bandelet coefficients by applying invertible one-dimensional decorrelation operators to said warped wavelet packet coefficients along selected directions of said wavelet packet warping grids.
 3. A signal processing method according to claim 2, further comprising the steps of: (a) quantizing said bandelet coefficients to produce quantized bandelet coefficients; and (b) encoding said quantized bandelet coefficients and said region and deformation parameters into a multiplexed data stream suitable for storage in a storage medium or for transmission over a transmission medium, said multiplexed data stream being a compressed representation of said n-dimensional digital input signal from which the n-dimensional warped signal is computed.
 4. A signal processing method according to claim 1, wherein the step of computing an n-dimensional warped signal includes computing an n-dimensional warped signal from said n-dimensional digital input signal, the n-dimensional warped signal including n-dimensional warped coefficients and n-dimensional signal warping grids from which the n-dimensional digital input signal can be reconstructed without any residual.
 5. A method of processing n-dimensional digital signals, n being an integer at least equal to 1, comprising the steps of: receiving an n-dimensional digital input signal; computing an n-dimensional warped signal from said n-dimensional digital input signal, the n-dimensional warped signal including n-dimensional warped coefficients and n-dimensional signal warping grids; computing warped wavelet packet coefficients and wavelet packet warping grids by applying an n-dimensional warped wavelet packet transform to said warped signal, with a binary tree where each node performs a one-dimensional warped subband processing along a respective dimension d, with 1≦d≦n; applying a bandeletisation to said warped wavelet packet coefficients and wavelet packet warping grids, wherein said bandeletisation comprises computing bandelet coefficients by applying invertible one-dimensional decorrelation operators to said warped wavelet packet coefficients along selected directions of said wavelet packet warping grids; quantizing said bandelet coefficients to produce quantized bandelet coefficients; and encoding said quantized bandelet coefficients and said region and deformation parameters into a multiplexed data stream suitable for storage in a storage medium or for transmission over a transmission medium, said multiplexed data stream being a compressed representation of said n-dimensional digital input signal from which the n-dimensional warped signal is computed; wherein said signal warping grids are computed from a warping geometry defined by region parameters specifying a partition of a signal support into a plurality of regions and deformation parameters specifying geometrical deformation functions respectively associated with said regions, whereby the geometrical deformation function associated with one of the regions provides positions of sampling points within said one of the regions; wherein n=3 and said n-dimensional digital input signal represents a video image sequence, and wherein the step of computing the parameters defining the warping geometry comprises estimating motion vectors within said video image sequence, and determining at least one of said n-dimensional geometrical deformation functions by applying a time displacement to a 2-dimensional geometrical deformation using said estimated motion vectors. 